Patent Publication Number: US-9852639-B2

Title: Generating a mission plan for capturing aerial images with an unmanned aerial vehicle

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. patent application Ser. No. 14/887,954, filed on Oct. 20, 2015. The aforementioned application is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     1. Technical Field 
     One or more embodiments of the present disclosure relate generally to digital mission plans for unmanned aerial vehicles (UAVs). More specifically, one or more embodiments of the present disclosure relate to systems and methods for generating a digital mission plan for capturing aerial images within a mission boundary utilizing a UAV. 
     2. Background and Relevant Art 
     Individuals and businesses increasingly utilize digital aerial images to capture information regarding target sites. Indeed, in light of advances in unmanned aerial vehicles (UAVs), digital aerial photography has become increasingly affordable, and therefore accessible, for individual, industrial, and commercial applications. For instance, individuals now commonly utilize UAVs to capture digital aerial images of homes, places of interest, or even recreational activities. 
     In many applications, individuals and businesses seek to capture digital aerial images utilizing digitally automated UAVs. For instance, in many industrial applications, such as mining or construction, business consumers seek updated aerial images or maps of a target site on a daily (or hourly) basis. Accordingly, clients seek systems that can regularly and automatically traverse a target site and capture aerial images. Some conventional automated flight systems have been developed to address this client demand, but these conventional systems have various problems and limitations. 
     For example, some conventional automated flight systems can capture digital aerial images of a target site, but fail to account for prohibited flight areas. For instance, many industrial and commercial clients seek to capture digital aerial images of a site but are prohibited from flying in certain areas as a result of no-flight zones, uncooperative adjacent property owners, sensitive areas, or physical obstacles (e.g., powerlines, buildings, etc.). Common automated UAV flight systems typically fail to generate flight plans that accommodate prohibited flight areas. 
     Similarly, many conventional automated UAV flight systems fail to accommodate irregular, arbitrary flight areas. For instance, some common automated flight systems can generate a flight plan that flies over a rectangular target area, but cannot generate a flight plan that stays within more complex, arbitrary polygons. For example, in many applications a target site has an irregular shape as a result of roads, property lines, sensitive air space, or other considerations. Known automated flight systems cannot accommodate such irregular target sites or generate flight plans that will stay within irregular flight areas. 
     Moreover, many conventional automated UAV flight systems generate a two-dimensional flight plan that provides geographical coordinate pairs (i.e., x and y coordinates) for a UAV flight. These systems, however, fail to adequately plan for changes in elevation with regard to the ground surface or obstacles. Thus, for example, common automated UAV flight systems cannot generate a mission plan with altitude data (i.e., changing z coordinates) that properly accounts for elevation data with regard to the target site. 
     Finally, many common automated flight systems are time consuming and difficult to use, as well as rigid, providing a user with very few options in how a UAV will traverse a target site. Users desire automated flight systems that generate flight missions quickly and intuitively as well as providing flexibility in handling a variety of target sites, site conditions, etc. Common systems struggle to satisfy user desire for fast, simple, and flexible operation, particularly in light of the fact that a near-infinite number of possibilities exist with regard to traversing any given target site to capture digital aerial images utilizing a UAV. 
     Accordingly, a number of problems and disadvantages exist with conventional systems for creating a mission plan for a UAV to capture digital aerial images of a target site. 
     BRIEF SUMMARY 
     Embodiments of the present disclosure provide benefits and/or solve one or more of the foregoing or other problems in the art with systems and methods for generating UAV mission plans. For example, in one or more embodiments, systems and methods solidify less volatile portions of a flight mission before applying one or more algorithms to identify an optimal or near-optimal means of traversing a target site. Specifically, the disclosed systems and methods generate a plurality of flight legs for traversing the target site and then identify efficient flight plans based on the generated flight legs. 
     For example, in one or more embodiments, a disclosed system identifies a mission boundary defining a UAV flight area, wherein the mission boundary encompasses a target site for capturing a plurality of aerial images by a UAV. Then, the system generates flight legs for the UAV flight area. In particular, the system generates flight legs separated by a leg spacing based on one or more characteristics of the UAV, where each flight leg intersects the mission boundary at two endpoints. In addition, the system also identifies flight vertices comprising corners of the mission boundary and the endpoints for each of the flight legs. With the flight vertices in hand, the system builds a flight path that does not extend beyond the mission boundary by combining the flight legs utilizing the flight vertices. Moreover, the disclosed system generates a mission plan based on the flight path, the mission plan comprising computer-executable instructions for causing the UAV to capture aerial images of the target site in accordance with the flight path. 
     By generating a flight path within the missionary boundary utilizing the flight legs, in one or more embodiments, the disclosed systems and methods can avoid prohibited areas. Thus, for example, a system can define a mission boundary that corresponds to a no-flight zone and generate a flight plan that remains within the mission boundary. Moreover, by building a flight plan utilizing the flight vertices and the flight legs, one or more embodiments of the disclosed systems and methods can accommodate irregular, arbitrary mission boundaries. For instance, one or more embodiments of the disclosed systems and methods can generate a flight path that stays within convex polygons, concave polygons, polygons with a variety of corners, or polygons with sides of varying lengths and angles. 
     In addition, in one or more embodiments, the disclosed systems and methods can also generate a mission plan with altitude data that takes into consideration variations in elevation across a target site. In particular, in one or more embodiments, the disclosed systems and methods access elevation data with regard to a target site and generate altitude data in the mission plan based on the elevation data across the target site. 
     Furthermore, by identifying flight legs and then building a flight path from the identified flight legs utilizing one or more linking algorithms, the disclosed systems and methods can generate flight missions quickly (i.e., with reduced processing time). Moreover, the disclosed systems and methods provide a user interface that allows users to flexibly and intuitively generate flight missions with regard to a variety of target sites and site conditions. 
     Additional features and advantages of exemplary embodiments of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such exemplary embodiments. The features and advantages of such embodiments may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features will become more fully apparent from the following description and appended claims, or may be learned by the practice of such exemplary embodiments as set forth hereinafter. The foregoing summary is not an extensive overview, and it is not intended to identify key elements or indicate a scope. Rather the foregoing summary identifies aspects of embodiments as a prelude to the detailed description presented below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order to describe the manner in which the above recited and other advantages and features of the invention can be obtained, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings. It should be noted that the figures are not drawn to scale, and that elements of similar structure or function are generally represented by like reference numerals for illustrative purposes throughout the figures. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which: 
         FIG. 1  illustrates a schematic diagram of a mission generation system in accordance with one or more embodiments; 
         FIG. 2  illustrates a schematic diagram of an exemplary environment in which the mission generation system of  FIG. 1  can operate in accordance with one or more embodiments; 
         FIG. 3A  illustrates a representation of a target site and a mission boundary in accordance with one or more embodiments; 
         FIG. 3B  illustrates a representation of generating flight legs in accordance with one or more embodiments; 
         FIG. 3C  illustrates a representation of generating flight legs and identifying flight leg endpoints and boundary corners in accordance with one or more embodiments; 
         FIG. 4  illustrates a representation of generating shortest connections between endpoint pairs in accordance with one or more embodiments; 
         FIG. 5A  illustrates a representation of building a flight path utilizing a nearest-neighbor linking algorithm in accordance with one or more embodiments; 
         FIG. 5B  illustrates a representation of continuing to build the flight path of  FIG. 5A  utilizing a nearest-neighbor linking algorithm in accordance with one or more embodiments; 
         FIG. 5C  illustrates a representation of the completed flight path of  FIG. 5A , built utilizing a nearest-neighbor algorithm in accordance with one or more embodiments; 
         FIG. 6A  illustrates a representation of building a flight path utilizing a cheapest-edge linking algorithm in accordance with one or more embodiments; 
         FIG. 6B  illustrates a representation of continuing to build the flight path of  FIG. 6A  utilizing a cheapest-edge linking algorithm in accordance with one or more embodiments; 
         FIG. 6C  illustrates a representation of the completed flight path of  FIG. 6A , built utilizing a cheapest-edge linking algorithm in accordance with one or more embodiments; 
         FIG. 7A  illustrates a computing device displaying a user interface including a target site in accordance with one or more embodiments; 
         FIG. 7B  illustrates the computing device and user interface of  FIG. 7A , including an inputted mission boundary in accordance with one or more embodiments; 
         FIG. 7C  illustrates the computing device and user interface of  FIG. 7A , including a mission plan in accordance with one or more embodiments; 
         FIG. 7D  illustrates the computing device and user interface of  FIG. 7A , including an obstacle in accordance with one or more embodiments; 
         FIG. 7E  illustrates the computing device and user interface of  FIG. 7A , including a mission plan modified based on the obstacle of  FIG. 7D  in accordance with one or more embodiments; 
         FIG. 8  illustrates a flowchart of a series of acts in a method of generating a mission plan in accordance with one or more embodiments; 
         FIG. 9  illustrates another flowchart of a series of acts in a method of generating a mission plan information in accordance with one or more embodiments; 
         FIG. 10  illustrates a block diagram of an exemplary computing device in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure includes various embodiments and features of a mission generation system and corresponding processes that produce mission plans for capturing aerial images of a target site by a UAV. In particular, in one or more embodiments the mission generation system generates flight legs for traversing a target site and identifies optimal or near-optimal connections between the flight legs utilizing one or more linking algorithms. The mission generation system can generate a flight path that remains within a mission boundary. Moreover, the mission generation system can incorporate the flight path into a mission plan for capturing aerial images of a target site utilizing a UAV. 
     For example, in one or more embodiments the mission generation system identifies a mission boundary defining a UAV flight area. Specifically, the mission generation system identifies a mission boundary that encompasses a target site for capturing a plurality of aerial images by a UAV. Then, in one or more embodiments, the mission generation system generates flight legs for the UAV flight area. In particular, the mission generation system generates flight legs that are separated by a leg spacing based on one or more characteristics of the UAV, where each flight leg intersects the mission boundary at two endpoints. In addition, in one or more embodiments, the mission generation system identifies flight vertices comprising corners of the mission boundary and the endpoints for each of the flight legs. Upon generating flight legs and identifying flight vertices, in one or more embodiments the mission generation system builds a flight path by combining the flight legs utilizing the flight vertices, wherein the flight path does not extend beyond or outside of the mission boundary. Moreover, in one or more embodiments, the mission generation system generates a mission plan based on the flight path, wherein the mission plan comprises computer-executable instructions for causing the UAV to capture aerial images of the target site in accordance with the flight path. 
     By identifying a mission boundary, generating flight legs within a mission boundary, and combining the flight legs, the mission generation system can generate a flight path that stays within a mission boundary and avoids prohibited flight areas. In particular, the disclosed mission generation system can generate a flight path that stays within complex mission boundaries of arbitrary shape and size. Thus, the mission generation system can generate a flight path that avoids impermissible flight areas adjacent to, or within, a target site, while still generating a mission plan that traverses the permissible flight areas of the target site to capture aerial images. 
     In one or more embodiments, the mission generation system identifies a mission boundary defining a UAV flight area by accessing digital flight area information. For instance, in one or more embodiments, the mission generation system identifies a mission boundary by accessing a digital database of flight zones, digital property boundaries, digital aerial images, or digital surveys. The mission generation system can utilize such digital flight area information to generate a mission boundary and define a UAV flight area. Because flight zones, property boundaries, features of aerial images, digital surveys, etc., are often complex geometrical shapes, mission boundaries often result in complex, arbitrary polygons. 
     Upon identifying a mission boundary, in one or more embodiments, the mission generation system generates a plurality of flight legs. In particular, the mission generation system can generate a plurality of flight legs that traverse the mission boundary while staying within the mission boundary. More specifically, in one or more embodiments, the mission generation system generates flight legs comprising a plurality of parallel straight lines within the mission boundary. In this manner, one or more embodiments of the mission generation system generate flight legs that intersect the mission boundary at two endpoints. 
     The mission generation system can generate flight legs at a particular flight angle. For example, the mission generation system can create flight legs at an angle that maximizes the average length of the flight legs traversing a UAV flight area. Additionally or alternatively, the flight angle can be predetermined, provided by a user, and/or based on environmental conditions (e.g., wind). 
     Similarly, the mission generation system can generate flight legs at a particular leg spacing. For example, in one or more embodiments, the mission generation system identifies a leg spacing between flight legs based on one or more characteristics of a UAV. For instance, the mission generation system can select a leg spacing based on a resolution or lens angle of a camera affixed to a UAV. Moreover, the mission generation system can base the leg spacing on a desired resolution for the captured aerial images. In this manner, the mission generation system can ensure that the flight legs traverse the mission boundary in a manner that will permit a camera affixed to the UAV to capture sufficiently-detailed images of the target site. 
     Moreover, in one or more embodiments, the mission generation system generates a flight path by combining flight legs utilizing one or more linking algorithms. In particular, the mission generation system can combine flight legs utilizing a nearest-neighbor linking algorithm, a cheapest-edge linking algorithm, a Christofides linking algorithm, and/or a brute-force linking algorithm. By utilizing these algorithms, the mission generation system can combine flight legs to form an optimal, or near-optimal, flight path from the flight legs and vertices of the mission boundary. 
     The mission generation system can apply different algorithms depending on various characteristics of the target site, the mission boundary, or the flight legs. For instance, in one or more embodiments, the mission generation system applies a nearest-neighbor linking algorithm upon determining that the mission boundary is convex. Similarly, in one or more embodiments, the mission generation system applies a cheapest-edge linking algorithm upon a determination that the mission boundary is concave. Moreover, in one or more embodiments, the mission generation system applies a brute-force linking algorithm upon a determination that the number of flight legs is less than a pre-determined flight leg threshold. Thus, the mission generation system can select algorithms particular to the features of a site or mission to produce more accurate results in a reduced amount of time. 
     Some of the algorithms utilized by the mission generation system link flight legs together based on the shortest connection between two identified endpoints. Accordingly, in one or more embodiments, the mission generation system also calculates the shortest connections between a plurality of pairs of endpoints. Specifically, the mission generation system can identify the shortest connections between each endpoint of a plurality of flight legs within a mission boundary. The mission generation system can utilize the shortest connections to combine the flight legs into a flight path. 
     It will be appreciated that target sites may include terrain with significant changes in elevation. These elevation changes can have a significant impact on aerial images. For example, the resolution and scope (e.g., width) of aerial images may change if the distance between the ground and a camera changes. Similarly, elevation changes can have a significant impact on a UAV (e.g., the UAV may need to adjust altitude to avoid collision with other objects). Accordingly, as mentioned previously, the mission generation system can generate a mission plan based on elevation data with regard to a target site. For example, in one or more embodiments, the mission generation system applies altitude data to one or more flight legs. For instance, the mission generation system can create one or more waypoints within one or more flight legs to provide altitude data for a UAV mission plan. 
     The mission generation system can identify elevation data of a target site in a variety of ways. For instance, in one or more embodiments, the mission generation system identifies elevation data from a third-party resource. In other embodiments, the mission generation system can generate its own elevation data. For example, in one or more embodiments, the mission generation system can traverse a target site, capture a plurality of aerial images, and generate elevation data with regard to the target site from the plurality of aerial images. The mission generation system can build a mission plan based on the identified elevation data. 
     Incorporating target site elevation data can often be quite large and time consuming to process. Accordingly, in one or more embodiments, the mission generation system compresses elevation data. For instance, in one or more embodiments, the mission generation system transforms elevation data into an image to reduce the size and time required to transfer and utilize elevation data. Specifically, in one or more embodiments, the mission generation system transforms elevation data into RGB values of an image file. 
     The mission generation system can also obtain elevation data based on user input. For example, in one or more embodiments, a user can provide information regarding one or more obstacles. For instance, the mission generation system can receive information regarding location, shape, and/or elevation of one or more obstacles within a target site. In response, the mission generation system can modify a mission plan based on the obstacles. For example, the mission generation system can add waypoints with regard to one or more flight legs to fly over (or around) one or more obstacles within a mission boundary. 
     The mission generation system can also generate and modify a mission plan based on a variety of additional factors with regard to a UAV and a target site. For example, the mission generation system can generate a mission plan based on a flight range of a UAV, a battery life of a UAV, a speed of a UAV, and other factors. Similarly, the mission generation system can generate a mission plan that considers wind, temperature, and other environmental factors. Thus, for example, a UAV can generate a mission plan that divides a flight path based on battery life and the amount of wind. Moreover, the UAV can dynamically modify a mission plan based on actual measured factors, such as a measured battery level, measured environmental factors, or a measured flight course. 
     As used herein, the term “mission boundary” refers to a border of a flight area with regard to a target site. The mission boundary can be user-provided and/or generated based on user input and other data. In some embodiments, the term mission boundary may represent a border defining a permissible area in which a UAV is permitted to fly. For instance, the term mission boundary includes a border defining an area in which a UAV is permitted to fly to capture one or more aerial images. The term mission boundary also includes a border between a permissible area in which a UAV is permitted to fly and one or more impermissible areas in which a UAV is not permitted to fly, or in which flying the UAV is undesirable or dangerous. 
     As used herein, the term “target site” refers to a location on Earth. In particular, the term target site includes a location on Earth that a user seeks to capture in a plurality of aerial images. The term target site can include a construction site, a mining site, a particular property, a wilderness area, a disaster area, or other location. 
     As used herein, the term “flight legs” refers to a portion of a flight path. In particular, the term flight legs includes parallel portions of a flight path within a mission boundary. For instance, the term flight legs includes parallel portions of a flight path within a mission boundary, the portions arranged at a flight angle and separated by a leg spacing. 
     As used herein, the term “endpoints” refers to ends of a flight leg. In particular, the term endpoints includes two ends of a flight leg. For example, the term endpoints includes points where flight legs intersect a mission boundary. 
     As used herein, the term “vertices” refers to points along a mission boundary. In particular, the term vertices includes corners of a mission boundary and endpoints. For instance, the term vertices refers to points where flight legs intersect a mission boundary. Similarly, the term vertices refers to corners of a mission boundary representing a property corner, a corner of a no-flight zone, or some other corner. 
     As used herein, the term “flight path” refers to a route for a UAV. In particular, the term flight path includes a route for a UAV to traverse a UAV flight area. For instance, the term flight path includes a plurality of flight legs with connections between the flight legs. For example, a flight path can include a plurality of flight legs connected by the shortest connections between flight legs, as ascertained by one or more algorithms. 
     As used herein, the term “mission plan” refers to a plan for traversing a target site utilizing a UAV. A mission plan can include both location and altitude data for traversing a target site. A mission plan can include a plan for flying to and from a base location (such as a docking station) in traversing a target site. A mission plan can also include a plan for splitting up a flight based on a UAV range, a UAV battery life, or any other factors affecting flight of a UAV. 
     Turning now to  FIG. 1 , additional detail will be provided regarding components and capabilities of one or more embodiments of the mission generation system. In particular,  FIG. 1  shows a schematic diagram illustrating an example embodiment of a mission generation system  100 . As shown in  FIG. 1 , in one or more embodiments, the mission generation system  100  includes a mission boundary manager  102 , a flight leg facility  104 , a flight path generator  106 , a mission generator  108 , an elevation data manager  110 , and a storage manager  112 . Moreover, the storage manager  112  may also include, as shown, flight mission data  114 , elevation data  116 , flight area data  118 , vertex data  120 , and UAV data  122 . 
     As just mentioned, and as illustrated in  FIG. 1 , the mission generation system  100  may include the mission boundary manager  102 . The mission boundary manager  102  can create, generate, provide, modify, access, and/or manage one or more mission boundaries. For instance, the mission boundary manager  102  can generate a mission boundary defining a UAV flight area with regard to a target site. 
     The mission boundary manager  102  can access and utilize digital flight area information to generate one or more mission boundaries. As used herein, the term “digital flight area information” refers to any information bearing on a UAV flight area. For example, the mission boundary manager  102  can access digital flight area information comprising one or more digital flight zones (e.g., no-fly zones with respect to airports, military bases, national parks, temporary flight restriction zones), property boundaries (e.g., digital recorded property lines, digital section maps, digital government boundary maps), roads, utilities (e.g., power lines/poles), digital aerial images (e.g., digital aerial photographs, satellite images), digital maps, digital surveys (e.g., digital boundary survey, PLATs, on-site surveys), electronic plans (digital construction plans, improvement plans, as-built plans), or other digital information. 
     The mission boundary manager  102  can access digital flight area information from a variety of sources. For instance, the mission boundary manager  102  can access digital flight area information from a third-party, such as a municipality that provides digital property information. The mission boundary manager  102  can also access stored digital flight area information from flight area data  118 . For instance, the mission boundary manager  102  can access a plurality of previously-captured aerial images from flight area data  118 . The mission boundary manager  102  can also obtain digital flight area information from a computer-readable storage medium or via the Internet. 
     As mentioned, in one or more embodiments, the mission boundary manager  102  utilizes digital flight area information to generate a mission boundary. For instance, in one or more embodiments, the mission boundary manager  102  detects a target site, and defines mission boundaries with regard to the target site based on digital flight area information. For example, a user can select a target site, the mission boundary manager  102  can access aerial images, property lines, and other digital flight area information with regard to the selected target site, and the mission boundary manager  102  can suggest to the user potential mission boundaries for the target site. Additionally or alternatively, a user can outline a mission boundary via user input, and the mission boundary manager  102  can suggest a more accurate mission boundary based on digital flight area information. 
     In other embodiments, the mission boundary manager  102  can define a mission boundary based on user input. For instance, in one or more embodiments, the mission boundary manager  102  provides for display a digital map or digital aerial image to a user and receives user input of a mission boundary in relation to the digital map or digital aerial image. For example, the user can draw the mission boundary over the displayed digital map or image. 
     The mission boundary manager  102  can generate a mission boundary of any size, type, or shape. For instance, the mission boundary manager  102  can generate a mission boundary manager comprising a polygon with any number of corners and sides. The mission boundary manager  102  can generate mission boundaries comprising convex polygons or concave polygons. The mission boundary manager  102  can also generate mission boundaries comprising curves, arcs, or circles. 
     Moreover, the mission boundary manager  102  can generate multiple mission boundaries to define a UAV flight area. For instance, in circumstances where a UAV flight area encompasses a no flight area, the mission boundary manager  102  can generate an outer mission boundary and an inner mission boundary (e.g., a donut shape). For example, the mission boundary manager  102  can define an outer mission boundary with a first shape (e.g., a ten sided polygon) and an inner mission boundary with a second shape (e.g., a square). Thus, for example, if a target site contains a secure area that a UAV is not permitted to access, the mission boundary manager  102  can exclude the secure area from the UAV flight area by defining an inner mission boundary around the secure area and an outer mission boundary area with regard to the perimeter of the target site. The mission boundary manager  102  can generate any number of inner/outer mission boundaries, depending on the particular target site or embodiment. 
     The mission boundary manager  102  can also modify one or more mission boundaries. For example, the mission boundary manager  102  can determine a change in digital flight area information and revise one or more mission boundaries. For instance, a temporary flight restriction may initially prohibit UAV flight in a certain area adjacent to a target site. At a later point in time, the temporary flight restriction may be lifted. The mission boundary manager  102  can detect a change in the temporary flight restriction, and modify the mission boundary accordingly (e.g., to enlarge the UAV flight area to reflect removal of the temporary flight restriction). 
     Similarly, the mission boundary manager  102  can detect changes to property lines, changes in aerial images, changes in other digital flight area information, or additional user input with regard to a UAV flight area. Then, the mission boundary manager  102  can modify a mission boundary based on the detected changes. 
     For example, in one or more embodiments, the mission boundary manager  102  can create an initial mission boundary prior to a first UAV flight of a target site. Thereafter, the UAV can capture aerial images of the target site and the mission generation system  100  can generate a model (e.g., a 3D model) or other representation of the target site. The mission boundary manager  102  can modify the initial mission boundary based on the generated targeted site model (e.g., move a mission boundary edge based on a location of a property line, if the aerial images and/or target site model provide a more accurate location of the property line). 
     As shown in  FIG. 1 , the mission generation system  100  also includes the flight leg facility  104 . The flight leg facility  104  can create, generate, select, provide, access, and/or manage one or more flight legs. In particular, the flight leg facility  104  can generate flight legs within a mission boundary (e.g., a mission boundary provided by the mission boundary manager  102 ). 
     In one or more embodiments, the flight leg facility  104  generates parallel flight legs. For instance, the flight leg facility  104  can generate parallel flight legs that traverse a target site or a UAV flight area. Moreover, the flight leg facility  104  can generate parallel flight legs that traverse a target site and stay within a mission boundary. 
     For example, in one or more embodiments, the flight leg facility  104  generates flight legs by calculating a centroid of a UAV flight area (i.e., the area encompassed by the mission boundary). The flight leg facility  104  can create initial flight legs to fill the UAV flight area that are offset based on leg spacing from the centroid (e.g., the initial flight leg can be offset by half a leg spacing from the centroid in a direction perpendicular to a flight leg angle) of the UAV flight area and oriented in a particular direction. The flight leg facility  104  can also identify portions of the initial flight legs that lie within the UAV flight area and portions of the initial flight legs that fall outside the UAV flight area. In one or more embodiments, the flight leg facility  104  discards the portions of the initial flight legs that fall outside the UAV flight area. Moreover, in one or more embodiments, the flight leg facility  104  generates flight legs based on the portions of the initial flight legs that fall within the UAV flight area. 
     As mentioned, the flight leg facility  104  can generate flight legs with one or more leg spacings. For instance, in at least one embodiment, the flight leg facility  104  can generate flight legs with an equal leg spacing between flight legs (i.e., flight legs that are spaced an equal distance apart). By providing equal leg spacing, in some embodiments, the mission generation system  100  can enable a UAV to capture digital aerial images that overlap sufficiently and by an equal (or near equal) amount. 
     In other embodiments, the flight leg facility  104  can generate flight legs with different leg spacings. For instance, the mission generation system  100  can identify a portion of the target site that is particularly critical to a client or particularly difficult to accurately capture (e.g., an area of significant elevation change or an area containing covered or hidden features). In this case, the flight leg facility  104  can modify the leg spacing to emphasize the portion of the target site. In particular, the flight leg facility  104  can apply a smaller leg spacing with regard to the portion of the target site. By applying a smaller spacing, the mission generation system  100  can enable a UAV to capture digital aerial images that overlap by a greater amount and provide greater detail with regard to the portion of the target site. 
     The flight leg facility  104  can select a leg spacing based on a variety of factors. For example, the flight leg facility  104  can select leg spacing based on one or more characteristics of a UAV. For instance, the flight leg facility  104  can select a leg spacing based on flight capabilities of a UAV, such as a maximum (or recommended) flight altitude, a maximum (or recommended) flight speed of a UAV, or other characteristics. For instance, a UAV with a capability to fly only at lower altitudes may require a smaller leg spacing than a UAV with a capability to fly at a higher altitude. 
     In addition, the flight leg facility  104  can select leg spacing based on other characteristics of a UAV, such as characteristics of one or more cameras affixed to a UAV. For example, the flight leg facility  104  can select leg spacing based on the resolution of a camera or the resolution of digital images resulting from use of the camera. Similarly, the flight leg facility  104  can select leg spacing based on a camera lens angle or a width of digital images resulting from use of the camera. For instance, in one or more embodiments, the flight leg facility  104  can select a wider leg spacing based on a determination that a camera aboard a UAV has a higher resolution and wider angle lens than a camera aboard another UAV. 
     The flight leg facility  104  can also select leg spacing based on a desired aerial image resolution. For instance, in one or more embodiments, a user can provide a desired resolution of aerial images resulting from a UAV flight. The flight leg facility  104  can utilize the desired aerial image resolution (e.g., along with the capabilities of the UAV/camera) to select a leg spacing. 
     In addition to leg spacing, the flight leg facility  104  can also select one or more flight angles associated with flight legs. For instance, with regard to embodiments that utilize parallel flight legs, the flight leg facility  104  can identify a flight angle that controls the direction of the parallel flight legs. The flight leg facility  104  can select a flight angle based on a variety of factors. 
     For instance, in one or more embodiments, the flight leg facility  104  selects a flight angle that reduces (or minimizes) the amount of flight time. For example, the flight leg facility  104  can select a flight angle that reduces (e.g., minimizes) the number of flight legs or the number of connections required between flight legs. Similarly, the flight leg facility  104  can select a flight angle that increases (e.g., maximizes) the length of one or more flight legs. For example, the flight leg facility  104  can select a flight angle that increases (e.g., maximizes) the average length of all flight legs. 
     The flight leg facility  104  can also select a flight angle based on one or more environmental characteristics. For instance, the flight leg facility  104  can select a flight angle based on a direction of wind (e.g., select a flight angle that is parallel to the wind to avoid being blown off course or select a flight angle perpendicular to the wind to avoid variations in UAV speed). Similarly, the flight leg facility  104  can select a flight angle based on a position of the sun (e.g., select a flight angle that is towards the sun to avoid glare from objects perpendicular to flight leg). 
     In some embodiments, the flight leg facility  104  generates non-parallel flight legs. For instance, the flight leg facility  104 , can generate flight legs that are not parallel in order to circumvent obstacles, capture images of elevated objects, obtain additional images (or images that overlap by a greater degree) with regard to certain portions of a target site, obtain images of a greater resolution with regard to a certain portion of a site, or for a variety of other reasons. 
     In generating non-parallel flight legs, the flight leg facility  104  can identify and utilize a plurality of flight angles. For example, the flight leg facility  104  can utilize a first flight angle for a first flight leg, a second flight angle for a second flight leg (e.g., to move closer to a desired area of a target site), and then utilize the first flight angle for a third flight leg. 
     In one or more embodiments, the flight leg facility  104  can also generate non-linear flight legs. For example, the flight leg facility  104  can generate flight legs that are circular, curvilinear, parabolic, logarithmic, zigzagged, or some other shape or pattern. In one or more embodiments, the flight leg facility  104  identifies the shape or pattern of flight legs based on the amount of time to traverse the flight legs. For example, in one or more embodiments, the flight leg facility  104  identifies the shape or pattern of flight legs that maximize the length of flight legs and/or minimizes the number of flight legs. In additional or alternative embodiments, the flight leg facility  104  can generate flight legs that follow the contours and/or shapes of one or more topographical features (e.g., hills) or structures (e.g., buildings). 
     In other embodiments, the flight leg facility  104  identifies a flight leg shape or pattern based on user input. For instance, in one or more embodiments, the user can select a desired shape of a flight leg. In other embodiments, a certain client may require a UAV to traverse a specific pattern in covering a target site. For example, a client may want the UAV to follow a plurality of roads that traverse the target site. In such circumstances, the flight leg facility  104  can generate flight legs based on user input of the flight legs. 
     As illustrated in  FIG. 1 , the mission generation system  100  also includes the flight path generator  106 . The flight path generator  106  can create, combine, generate, modify, or access one or more flight paths. For instance, the flight path generator  106  can combine a plurality of flight legs to form a flight path. In particular, the flight path generator  106  can combine a plurality of flight legs by identifying connections between the flight legs to form a flight path. 
     In one or more embodiments, the flight path generator  106  identifies vertices. In particular, the flight path generator  106  can identify points where flight legs intersect a mission boundary (i.e., endpoints) as well as corners of mission boundaries. The flight path generator  106  can identify vertices to assist in generating an optimal or near-optimal flight path from a plurality of flight legs. 
     Specifically, in one or more embodiments, the flight path generator  106  utilizes the vertices to generate shortest connections. In particular, the flight path generator  106  can calculate the shortest connection between any two vertices (e.g., the shortest connection between any two endpoints). For instance, in one or more embodiments, the flight path generator  106  utilizes a Floyd-Warshall algorithm to obtain the shortest connection between any two identified endpoints in polynomial time. 
     For example, the flight path generator  106  can identify a shortest connection between a first endpoint and a second endpoint. Moreover, with regard to two endpoints that do not have a common line of sight (e.g., cannot be connected by a straight line that stays within the mission boundary), the flight path generator  106  can identify the shortest connection between the two endpoints while staying within the mission boundary. In particular, the flight path generator  106  can utilize the one or more vertices to connect two endpoints that do not have a common line of sight. 
     In one or more embodiments, the flight path generator  106  can generate a shortest connection database that includes the shortest connection between one or more (or all) pairs of endpoints with regard to a mission boundary. In such circumstances, the flight path generator  106  can store the shortest connection database (e.g., within the vertex database  120 ). The flight path generator  106  can also utilize the shortest connection database to combine a plurality of flight legs to generate a flight path. 
     Specifically, the flight path generator  106  can utilize a variety of algorithms to combine flight legs and connections to generate a flight path. For instance, as mentioned above, the flight path generator  106  can utilize a nearest-neighbor linking algorithm, a cheapest-edge linking algorithm, a Christofides linking algorithm, or a brute-force linking algorithm. 
     As just mentioned, the flight path generator  106  can utilize a nearest-neighbor linking algorithm to generate a flight path. Specifically, and as discussed in greater detail below, in one or more embodiments the nearest-neighbor linking algorithm generates a flight path by identifying a starting endpoint, proceeding through a flight leg, and identifying the nearest endpoint. For example, in one or more embodiments, the flight path generator  106  starts at a first endpoint of a first flight leg, proceeds to the second endpoint of the first flight leg, and then proceeds to the nearest unvisited endpoint of a second flight leg. In one or more embodiments, the flight path generator  106  identifies the nearest unvisited endpoint of the second flight leg by referencing the shortest connection database (e.g., by identifying the shortest connection in the shortest connection database originating from the second endpoint of the first flight leg). 
     Upon proceeding to the nearest unvisited endpoint of the second flight leg, in one or more embodiments the nearest-neighbor linking algorithm proceeds to the second endpoint of the second flight leg. The flight path generator  106  again identifies the nearest unvisited endpoint of a third flight leg. In one or more embodiments, the nearest-neighbor linking algorithm generates a possible flight path by continuing in this pattern until traversing all of the flight legs. 
     In one or more embodiments, the flight path generator  106  can generate multiple possible flight paths utilizing the nearest-neighbor linking algorithm. Specifically, the flight path generator  106  can test every endpoint as a starting point and calculate a possible flight path for every endpoint utilizing the nearest-neighbor linking algorithm. Upon calculating a possible flight path originating from every endpoint, the flight path generator  106  can select the resulting flight path with the shortest length. 
     In addition to utilizing a nearest-neighbor linking algorithm, in one or more embodiments, the flight path generator  106  utilizes a cheapest-edge linking algorithm. In one or more embodiments, the cheapest-edge linking algorithm builds a flight path beginning with all of the flight legs (e.g., flight legs generated by the flight leg facility  104 ). Moreover, the cheapest-edge linking algorithm adds connections to the flight path by identifying a smallest connection. Specifically, the cheapest-edge linking algorithm identifies the smallest connection (e.g., from the smallest connection database) that does not result in a cycle and does not result in a vertex with more than two paths. In one or more embodiments, the cheapest-edge linking algorithm adds the identified smallest connection to the flight path. 
     In one or more embodiments, the cheapest-edge linking algorithm continues by identifying the next smallest connection (e.g., from the smallest connection database) that does not result in a cycle and does not result in a vertex with more than two paths and adds the identified smallest connection to the flight path. The cheapest-edge linking algorithm can continue this pattern until connecting all of the flight legs into a flight path. 
     As mentioned previously, the flight path generator  106  can also utilize a Christofides linking algorithm to build a flight path. A Christofides linking algorithm can compute a flight that is guaranteed to be within 150% of a minimum flight path length. One or more embodiments utilize a modified Christofides algorithm that creates a minimum spanning tree that contains all the flight legs. In other words, the flight path generator  106  can run a Christofides algorithm under the condition that it contains all flight legs. In this manner, in one or more embodiments, the flight path generator  106  builds a flight path. 
     In yet other embodiments, the flight path generator  106  can utilize a brute-force linking algorithm. In particular, a brute-force algorithm can test all permutations of route configurations and identify the absolute minimum. In other words, the flight path generator  106  can search all possible combinations of linking flight legs and identify the flight path that results in the minimum distance. 
     In some embodiments, the flight path generator  106  utilizes multiple algorithms. For instance, in one or more embodiments, the flight path generator  106  applies the nearest-neighbor linking algorithm, the cheapest-edge linking algorithm, and the Christofides linking algorithm, and selects the shortest resulting flight path. 
     In addition, in one or more embodiments, the flight path generator  106  utilizes different algorithms in response to different characteristics of a target site, mission boundary, or flight legs. For example, in one or more embodiments, the flight path generator  106  detects whether a mission boundary is convex or concave. If the mission boundary is convex, the flight path generator  106  applies a nearest-neighbor linking algorithm. If the mission boundary is concave, the flight path generator  106  applies a nearest-neighbor linking algorithm, a cheapest-edge linking algorithm, and/or a Christofides linking algorithm. 
     Similarly, in one or more embodiments, the flight path generator  106  detects a number of flight legs (or a number of endpoints) and selects an algorithm based on the number of flight legs. For example, in one or more embodiments, the flight path generator  106  compares the number of flight legs to a flight leg threshold. Based on the comparison, the flight path generator  106  can apply different algorithms. For example, in one or more embodiments if the number of flight legs falls below the flight leg threshold, the flight path generator  106  applies a brute-force linking algorithm. If, however, the number of flight legs meets or exceeds the flight leg threshold, the flight path generator  106  applies a different algorithm (e.g., a nearest-neighbor linking algorithm, a cheapest-edge linking algorithm, and/or a Christofides linking algorithm). 
     In one or more embodiments, the flight path generator  106  can also generate one or more flight paths while taking into consideration a location of a docking station. For instance, in one or more embodiments, a UAV may need to start at a docking station and discontinue a mission to return to a docking station (e.g., to recharge or exchange a battery). The flight path generator  106  can generate a flight path that takes into consideration starting at a docking station, a need to return to a docking station, and a location of the docking station. 
     For instance, in one or more embodiments, the flight path generator  106  can determine a location of a docking station. The flight path generator  106  can identify shortest connections between flight leg endpoints and the docking station. Moreover, the flight path generator  106  can utilize the shortest connections between the endpoints and the docking station to generate a connection from endpoints to the docking station. For example, the flight path generator  106  can identify a shortest connection between a docking station and a first endpoint to start, identify a shortest connection from a second endpoint to the docking station, and identify a shortest connection from the docking station to a third endpoint. 
     Moreover, in one or more embodiments, the flight path generator  106  can select a flight path based on the added distance traveling to and from the docking station. For instance, with regard to the nearest-edge linking algorithm, the flight path generator  106  can add a condition that requires a connection to a docking station after traversing a certain distance. Based on the condition, the flight path generator  106  can generate a flight path that traverses a number of flight legs, routes to a docking station, and then resumes traversing flight legs. Moreover, the flight path generator  106  can test a variety of starting points and connections, based on the condition requiring connection to a docking station, and select the shortest resultant flight path that satisfies the condition. 
     In addition to generating flight paths, the flight path generator  106  can also modify one or more flight paths. For example, in response to a break in a mission, the flight path generator  106  can generate a modified flight plan. For instance, the flight path generator  106  can determine what portions of the flight path have already been visited, and calculate a modified flight path for the remainder of the UAV flight area. For instance, the flight path generator  106  can identify a new starting position and new connections between flight legs based on a remaining portion of a UAV flight area that a UAV has not traversed. 
     Similarly, the flight path generator  106  can modify flight paths based on other updated or revised information. For instance, upon receiving a modified mission boundary, modified flight legs, or other information, the flight path generator  106  can modify one or more flight paths. 
     As illustrated in  FIG. 1 , in addition to the flight path generator  106 , the mission generation system  100  also includes the mission generator  108 . The mission generator  108  can create, generate, modify, and/or manage one or more mission plans. In particular, the mission generator  108  can create a mission plan based on one or more flight paths (e.g., flight paths provided by the flight path generator  106 ). 
     For example, based on a flight path, the mission generator  108  can create a mission plan that includes computer-executable instructions for causing a UAV to capture aerial images of the target site in accordance with the flight path. In particular, the mission generator  108  can transform a generated flight path to computer-executable instructions that a UAV can utilize to traverse a UAV flight area with regard to a target site. As one example, the mission generator  108  can generate a plurality of waypoints corresponding to, for example, the flight path, changes in flight direction, changes in flight altitude, and/or UAV battery levels (e.g., representing points where the UAV needs to “return to home” to recharge or receive a replacement battery). As will be explained in more detail below, each waypoint can include location information (e.g., X and Y coordinates) as well as altitude information (e.g., Z coordinates). 
     For example, the mission generator  108  can create a mission plan that includes digital altitude information. In particular, the mission generator  108  can add digital altitude information to a flight path based on digital elevation data related to a target site. For example, the mission generator  108  can access elevation data regarding a site (e.g., from the elevation data manager  110 ) and generate a mission plan based on the accessed information. 
     For instance, in one or more embodiments, the mission generator  108  adds altitude information to a mission plan so as to maintain a certain altitude from the ground. For example, the mission generator  108  can receive information that indicates a rise in elevation at a midpoint of a flight leg in a flight path. The mission generator  108  can add one or more waypoints to accommodate the rise in elevation. For instance, the mission generator  108  can add a waypoint to a flight path at a location before the jump in elevation (e.g., corresponding to a structure or other topographical feature having an abrupt change in elevation) and add a waypoint to the flight path at (or near) a location corresponding to the jump in elevation so that the altitude of the UAV approximates the jump in elevation in the target site. In additional or alternative examples, with respect to gradual elevation changes, the mission generator  108  can add a waypoint of a first altitude to a flight path at or near a first change in elevation (e.g., the bottom of an incline) and add another waypoint of second altitude to the flight path at or near a second change in elevation (e.g., the top of an incline), so that the flight of the UAV generally follows the underlying changes in elevation. 
     In one or more embodiments, the mission generator  108  maintains a certain altitude above the ground based on one or more characteristics of the UAV. For example, in one or more embodiments, the mission generator  108  may establish altitude data based on a resolution or lens angle of a camera affixed to a UAV. For instance, a client may desire digital aerial images of a particular resolution (e.g., 1 pixel per 5 cm) and a certain amount of overlap between aerial images (e.g., 60% side overlap and 50% forward overlap). Based on the characteristics of a camera affixed to the UAV, the UAV may need to maintain a certain altitude above the ground to obtain the desired resolution and/or overlap. The mission generator  108  can identify an altitude to maintain based on the desired resolution and/or overlap and the resolution, lens angle, and/or other characteristics of the camera affixed to the UAV. 
     The mission generator  108  can also create a mission plan based on one or more obstacles in a target site. For example, the mission generator  108  can detect an obstacle based on user input or based on elevation data. In response, the mission generator  108  can modify a flight path to avoid the obstacle. 
     For instance, the mission generator  108  can modify a flight path to fly over an obstacle. In particular, the mission generator  108  can add one or more waypoints to a flight path to adjust the altitude of a UAV. Specifically, the mission generator  108  can add waypoints such that the UAV maintains a certain distance from the obstacle. 
     In other embodiments, the mission generator  108  can modify a flight path to circumvent an obstacle. For example, if an obstacle is above a height threshold (e.g., a maximum flying altitude of the UAV or a height that will take too long to reach), the mission generator  108  can modify a flight path to circumvent the obstacle, rather than flying over the obstacle. Specifically, the mission generator  108  can identify an obstacle boundary and calculate a shortest connection around the obstacle. 
     For example, if the obstacle is a building, the mission generator  108  can identify building corners and identify a shortest connection around the building back to a flight path. The mission generator  108  can modify the flight path to navigate around the building utilizing the shortest connection. 
     Rather than simply circumventing an obstacle, the mission generator  108  can also modify the mission plan to capture aerial images of an obstacle. In particular, in response to identifying an obstacle, the mission generator  108  can generate a mission plan that orbits an obstacle to obtain digital aerial images of the obstacle. For example, the mission generator  108  can modify the mission plan to provide instructions for orbiting a building at various flight altitudes to obtain digital aerial images of the building. More specifically, the mission generator  108  can modify the mission plan to orbit the building at altitudes separated by a vertical spacing. Moreover, the mission generator  108  can include instructions in the mission plan to rotate a camera affixed to the UAV (or rotate the UAV itself) to capture digital aerial images of the building adjacent to the UAV during flight. 
     The mission generator  108  can also dynamically modify a mission plan during a mission. In particular, the mission generator  108  can receive information regarding a UAV or mission and modify the mission plan based on the additional information. For instance, in one or more embodiments, the mission generator  108  receives information regarding UAV speed, UAV altitude, UAV remaining battery life, UAV position, wind, temperature, or other information and modifies the mission based on the information. 
     Specifically, the mission generator  108  can determine that a battery utilized by a UAV for flight is losing charge at a faster rate than anticipated. In response, the mission generator  108  can modify the mission plan. In particular, the mission generator  108  can provide additional criteria to the flight path generator  106 , and receive a revised flight path that returns the UAV to a docking station. Moreover, the flight path generator  106  can identify a modified flight path to navigate after the docking station. The mission generator  108  can generate a modified mission plan based on the modified flight path from the flight path generator  106 . Additionally or alternatively, the mission generator  108  can add and/or move waypoints to the mission plan in order to incorporate the mission plan modifications. 
     In addition, as shown in  FIG. 1 , the mission generation system  100  also includes the elevation data manager  110 . The elevation data manager  110  can access, receive, analyze, generate, provide, or modify elevation data. For instance, the elevation data manager  110  can access and provide elevation data with regard to a target site. Moreover, the elevation data manager  110  can generate elevation data with regard to a site. In particular, the elevation data manager  110  can access or generate elevation data for utilization in creating a mission plan (e.g., generating altitude data for a mission plan by the mission generator  108 ). 
     The elevation data manager  110  can obtain elevation data from a variety of sources. For example, in one or more embodiments the elevation data manager  110  can obtain information from a third party resource. Specifically, in one or more embodiments, the elevation data manager  110  obtains elevation data from the United States National Aeronautics and Space Administration (NASA), such as data from the Shuttle Radar Topography Mission (SRTM). 
     In alternative embodiments the elevation data manager  110  generates elevation data. For example, in one or more embodiments the elevation data manager  110  can obtain a plurality of initial images of a target site (e.g., from an initial flight of the target site by a UAV). Utilizing the plurality of initial images of the target site, the elevation data manager  110  can generate elevation data. In particular, the elevation data manager  110  can utilize a plurality of initial images of a target site to generate a three-dimensional model (e.g., a three-dimensional point cloud) of the target site. In this manner, the elevation data manager  110  can generate current, site-specific elevation data. 
     The elevation data manager  110  can also transform, compress, or decompress elevation data. Indeed, elevation data can take a significant amount of memory and processing power to utilize or manipulate. Accordingly, the elevation data manager  110  can transform the elevation data (e.g., from a third-party source, such as NASA, or from a previously generated 3D model) to make the elevation data more manageable. In particular, in one or more embodiments, the elevation data manager  110  transforms the elevation data into an image. Specifically, in one or more embodiments, the elevation data manager  110  transforms the elevation data into a PNG file. In other embodiments, the elevation data manger  110  transform the elevation data into a JPEG, IMG, TIFF, GIF, BMP, or other image file. 
     More specifically, in one or more embodiments, the elevation data manager  110  utilizes an image file that stores information in the form of RGB (red, green blue) data. The elevation data manager  110  encodes the elevation into RGB data and embeds the information in the image file. In this manner, the elevation data manager  110  can significantly reduce the amount of space and processing power needed to utilize or manipulate elevation data. When needed, the elevation data manager  110  can also transform information from the encoded RGB data to more traditional elevation data. 
     Although described above with regard to RGB data, it will be appreciated that the elevation data manager  110  can also encode data utilizing other forms of image data. For example, the elevation data manager  110  can also encode data within image files utilizing LAB color data, or other image data types. 
     Moreover, as illustrated in  FIG. 1 , the mission generation system  100  also includes the storage manager  112 . The storage manager  112  maintains data for the mission generation system  100 . The storage manager  112  can maintain data of any type, size, or kind, as necessary to perform the functions of the mission generation system  100 . 
     As illustrated, the storage manager  112  may include flight mission data  114 . Flight mission data  114  includes mission plan information to enable a UAV to traverse a mission boundary and capture a plurality of images with regard to a target site. As described above, mission data  114  may include altitude data, one or more waypoints, a location of a docking station, and other information necessary for completion of a mission to capture a plurality of aerial images with regard to the target site. 
     Moreover, as shown in  FIG. 1 , the storage manager  112  may also include elevation data  116 . As mentioned above, elevation data  116  may include information regarding elevations of a target site. Elevation data  116  can include a plurality of images captured by a UAV and utilized to generate elevation data of a site. Elevation data  116  can also include elevation data obtained from any other source. Similarly, elevation data  116  can include elevation data compressed and embedded into one or more image files. Elevation data  116  may also include altitude data generated with regard to one or more mission plans. 
     As shown in  FIG. 1 , the storage manager  112  may also include flight area data  118 . Flight area data  118  includes digital flight area information (as described previously). For example, flight area data  118  can include flight zones, property boundaries, aerial images of a target site, electronic plans, survey data, or other digital flight area data. Flight area data  118  may also include one or more mission boundaries generated or received with regard to a target site. 
     Moreover, as illustrated in  FIG. 1 , the storage manager  112  may also include vertex database  120 . The vertex database  120  can include information regarding vertices. In particular, in one or more embodiments, the vertex database  120  includes information regarding mission boundary corners, flight leg endpoints, obstacle corners, or other vertices. Moreover, in one or more embodiments, the vertex database  120  includes information regarding connections between vertices, such as a shortest connection database. 
     Similarly,  FIG. 1  illustrates that the storage manager  112  also includes UAV data  122 . UAV data  122  includes information regarding one or more characteristics of one or more UAVs. For instance, UAV data  122  includes information regarding capabilities (e.g., speed, flight time, battery charge time, range, etc.) of a UAV. Similarly, UAV data  122  includes information regarding one or more cameras affixed to a UAV (e.g., resolution, lens angle). UAV data  122  also includes dynamic information regarding characteristics of a UAV during a mission, such as remaining battery life, flight speed, elevation, position, etc. UAV data also includes environmental data encountered by a UAV (e.g., wind, temperature, or sun location). 
     Each of the components  102 - 112  of the mission generation system  100  and their corresponding elements may be in communication with one another using any suitable communication technologies. It will be recognized that although components  102 - 112  are shown to be separate in  FIG. 1 , any of components  102 - 112  may be combined into fewer components (such as into a single component), divided into more components, or configured into different components as may serve a particular embodiment. Moreover, one or more embodiments of the mission generation system  100  may include additional components or fewer components than those illustrated in  FIG. 1 . 
     The components  102 - 112  and their corresponding elements can comprise software, hardware, or both. For example, the components  102 - 112  and their corresponding elements can comprise one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices. When executed by the one or more processors, the computer-executable instructions of the mission generation system  100  can cause one or more computing systems (e.g., one or more server devices) to perform the methods and provide the functionality described herein. Alternatively, the components  102 - 112  can comprise hardware, such as a special purpose processing device to perform a certain function or group of functions. Additionally or alternatively, the components  102 - 112  can comprise a combination of computer-executable instructions and hardware. 
     Furthermore, the components  102 - 112  of the mission generation system  100  and their corresponding elements may, for example, be implemented as one or more stand-alone applications, as one or more modules of an application, as one or more plug-ins, as one or more library functions or functions that may be called by other applications, and/or as a cloud-computing model. Thus, the components  102 - 112  of the mission generation system  100  and their corresponding elements may be implemented as a stand-alone application, such as a desktop or mobile application. Furthermore, the components  102 - 112  of the mission generation system  100  may be implemented as one or more web-based applications hosted on a remote server. Alternatively or additionally, the components of the mission generation system  100  may be implemented in a suite of mobile device applications or “apps.” 
     Turning now to  FIG. 2 , further information will be provided regarding implementation of the mission generation system  100 . Specifically,  FIG. 2  illustrates a schematic diagram of one embodiment of an exemplary system environment (“environment”)  200  in which the mission generation system  100  can operate. As illustrated in  FIG. 2 , the environment  200  can include client devices  202   a - 202   b , a UAV  204 , a docking station  206 , a network  208 , and server(s)  210 . The client devices  202   a - 202   b , the UAV  204 , the docking station  206 , the network  208 , and the server(s)  210  may be communicatively coupled with each other either directly or indirectly (e.g., through network  208 ). The client devices  202   a - 202   b , the UAV  204 , the docking station  206 , the network  208 , and the server(s)  210  may communicate using any communication platforms and technologies suitable for transporting data and/or communication signals, including any known communication technologies, devices, media, and protocols supportive of remote data communications, examples of which will be described in more detail below with respect to  FIG. 10 . 
     As just mentioned, and as illustrated in  FIG. 2 , the environment  200  can include the client devices  202   a - 202   b . The client devices  202   a - 202   b  may comprise any type of computing device. For example, the client devices  202   a - 202   b  may comprise one or more personal computers, laptop computers, mobile devices, mobile phones, tablets, special purpose computers, TVs, or other computing devices. In one or more embodiments, the client devices  202   a - 202   b  may comprise computing devices capable of communicating with the UAV  204 , the docking station  206 , and/or the server(s)  210 . More specifically, in one or more embodiments, a pilot may utilize the client device  202   b  to locally control and/or communicate with the UAV  204 . The client devices  202   a - 202   b  may comprise one or more computing devices as discussed in greater detail below with regard to  FIG. 10 . 
     Moreover,  FIG. 2  also illustrates that the environment  200  can include the UAV  204 . As used herein, the term “UAV” or “unmanned aerial vehicle” refers to an aircraft that can be piloted autonomously or remotely by a control system. Accordingly, the UAV  204  may comprise any type of UAV, including a micro UAV, low altitude UAV, or high altitude UAV, whether autonomously or remotely piloted. Similarly, the UAV  204  may include multi-rotor UAVs, single-rotor UAVs, blimp UAVs, or other types of UAVs. In particular, the UAV  204  may include an onboard computer that controls the autonomous flight of the UAV  204 . 
     In at least one embodiment, the UAV  204  is a multi-rotor vehicle, such as a quadcopter, and includes a carbon fiber shell, integrated electronics, a battery bay, a global positioning system (“GPS”) receiver, a fixed or swappable imaging system (e.g., a digital camera), and various additional sensors and/or receivers. The UAV  204  may contain one or more computer-readable storage media and/or one or more processors with instructions stored thereon that, when executed by the one or more processors cause the UAV  204  to perform functions described herein. 
     Alternatively or additionally, the environment  200  may include the docking station  206 . The docking station  206  may be utilized to land, store, charge, guide, or repair the UAV  204 . In particular, in one or more embodiments, the docking station  206  can charge or replace batteries exhausted by the UAV  204  during flight. Moreover, the docking station  206  may be utilized to communicate with the UAV  204  prior to, during, or after a flight. 
     As illustrated in  FIG. 2 , the client devices  202   a - 202   b , the UAV  204 , the docking station  206 , and/or the server(s)  210  may communicate via the network  208 . The network  208  may represent a network or collection of networks (such as the Internet, a corporate intranet, a virtual private network (VPN), a local area network (LAN), a wireless local network (WLAN), a cellular network, a wide area network (WAN), a metropolitan area network (MAN), or a combination of two or more such networks. Thus, the network  208  may be any suitable network over which the client devices  202   a - 202   b  (or other components) may access the server(s)  210  or vice versa. The network  208  will be discussed in more detail below with regard to  FIG. 10 . 
     Moreover, as illustrated in  FIG. 2 , the environment  200  also includes the server(s)  210 . The server(s)  210  may generate, store, receive, and/or transmit any type of data, including flight mission data  114 , elevation data  116 , flight area data  118 , vertex database  120 , and UAV data  122 . 
     For example, the server(s)  210  receive data from the client device  202   a  and send the data to the client device  202   b , the UAV  204 , and/or the docking station  206 . In one example embodiment, the server(s)  210  comprise a data server. The server(s)  210  can also comprise a communication server or a web-hosting server. Additional details regarding the server(s)  210  will be discussed below with respect to  FIG. 10 . 
     Although  FIG. 2  illustrates two client devices  202   a - 202   b , the single UAV  204 , and the single docking station  206 , it will be appreciated that the client devices  202   a - 202   b , the UAV  204 , and the docking station  206  can represent any number of computing devices, UAVs, or docking stations (fewer or greater than shown). Similarly, although  FIG. 2  illustrates a particular arrangement of the client devices  202   a - 202   b , the UAV  204 , the docking station  206 , the network  208 , and the server(s)  210 , various additional arrangements are possible. 
     For example, the client device  202   b , the UAV  204  and/or the docking station  206  may communicate directly one with another via a local connection  212 . The local connection  212  may comprise any recognized form of wired or wireless communication. For example, in one or more embodiments the client device  202   b  may include a mobile computing device (e.g., tablet) utilized by a UAV operator to communicate with the UAV  204  and the docking station  206  using BLUETOOTH technology. 
     By way of an additional example, in one or more embodiments, the client device  202   a  can request and receive from the server(s)  210  elevation data  116 , flight area data  118 , and UAV data  122  via the network  208 . The client device  202   a  can identify a mission boundary, create flight legs, and generate one or more flight paths (e.g., via the mission boundary manager  102 , the flight leg facility  104 , and the flight path generator  106  implemented on the client device  202   a ). Moreover, the client device  202   a  can generate a mission plan from the one or more flight paths (e.g., via the mission generator  108  and the elevation data manager  110  implemented on the client device  202   a ). The client device  202   a  can transmit the mission plan to the client device  202   b  via the network  208 , and the client device  202   b  can convey the mission plan to the UAV  204 . The UAV  204  can execute the mission plan with regard to a target site. In addition, the UAV  204  can return to the docking station  206  during the mission to recharge or replace a battery utilized for operation of the UAV  204 . In one or more embodiments, the UAV  204  can detect environmental information, and convey the environmental information to the client device  202   b , which can modify the mission plan based on the environmental information (e.g., via the flight path generator  106 , the mission generator  108 , and the elevation data manager  110  implemented on the client device  202   b ). Moreover, in one or more embodiments, client device  202   b  can modify the mission plan based on completed portions of the mission plan, or other factors. Ultimately, the UAV  204  can capture a plurality of digital aerial images of a target site based on the generated/modified mission plan. 
     As illustrated by the previous example embodiment, the mission generation system  100  may be implemented in whole, or in part, by the individual elements  202   a - 210  of the environment  200 . Although the previous example, described certain components of the mission generation system  100  implemented with regard to certain components of the environment  200  (e.g., implementation of the flight leg facility  104  via the client device  202   a ), it will be appreciated that components of the mission generation system  100  can be implemented in any of the components of the environment  200 . For example, the mission generation system  100  may be implemented entirely on the client device  202   b . Similarly, the mission generation system  100  may be implemented on the server(s)  210 . Alternatively or additionally, different components and functions of the mission generation system  100  may be implemented separately among the client devices  202   a - 202   b , the UAV  204 , the docking station  206 , the network  208 , and the server(s)  210 . For instance, the mission generator  108  may be implemented as part of the docking station  206 , the elevation data manager  110  may be implemented as part of the UAV  204 , the flight path generator  106  may be implemented as part of the client device  202   b , and the storage manager  112  may be implemented as part of the server(s)  210 . 
     Turning now to  FIGS. 3A-3C , additional detail will be provided regarding calculating flight legs within a mission boundary in accordance with one or more embodiments. In particular,  FIG. 3A  illustrates an aerial view of a target site  300  and a neighboring property  302 .  FIG. 3A  also illustrates a representation of a mission boundary  304  created by the mission generation system  100  that defines a UAV flight area  308 . 
     Specifically, in the embodiment of  FIG. 3A , the mission generation system  100  creates the mission boundary  304  based on a combination of user input and digital flight area information. In particular, the mission generation system  100  identifies a digital property map outlining the property boundary of the target site  300  and the neighboring property  302 . The mission generation system  100  suggests the property boundary of the target site  300  to a user as part of a proposed mission boundary. Based on user selection of the property boundary, the mission generation system  100  creates the mission boundary  304 . Moreover, the mission generation system  100  identifies the area within the mission boundary  304  as the UAV flight area  308 . 
     As described above, in other embodiments, the mission generation system  100  can utilize other digital flight area information to generate a mission boundary. For instance, the mission generation system  100  could also generate the mission boundary  304  by analyzing a digital aerial image, identifying a road  306 , and suggesting an edge of the road  306  as a portion of a mission boundary. Similarly, the mission generation system can analyze digital flight zones and determine that an edge of the target site  300  abuts a wilderness area that prohibits UAV flights. 
     Although the UAV flight area  308  and the target site  300  largely coincide with regard to the embodiment of  FIG. 3A , it will be appreciated that in one or more embodiments, the UAV flight area  308  will differ from the target site  300 . For example, in some embodiments, portions of the target site  300  may not allow for UAV flight (e.g., sensitive areas of a target site where UAV flights are prohibited); thus, the UAV flight area  308  may be smaller than the target site  300 . In some embodiments, however, the UAV flight area  308  may be larger than the target site  300  (e.g., where a UAV is permitted to fly along adjacent properties outside of the target site in capturing digital aerial images). 
     Upon identifying a mission boundary, in one or more embodiments the mission generation system  100  generates flight legs. In particular, in one or more embodiments, the mission generation system  100  generates flight legs by calculating a centroid of a UAV flight area, and then creating flight legs at a specified flight angle and leg spacing from the centroid. 
     For example,  FIG. 3B  illustrates a representation of generating flight legs in accordance with one or more embodiments. In particular,  FIG. 3B  illustrates the mission boundary  304  generated with regard to the target site  300 . Moreover,  FIG. 3B  illustrates a centroid  310  of the UAV flight area  308  calculated by the mission generation system  100 . 
     Based on the centroid  310 , the mission generation system  100  can create initial flight legs  318   a - 318   n  utilizing the flight angle  316  and the leg spacing  312 . In particular, the mission generation system  100  can generate initial flight legs  318   c ,  318   d  by drawing lines oriented at the flight angle  316  a distance of one half of the leg spacing  312  from the centroid  310  (i.e., a distance perpendicular to the flight angle  316 ). In this manner, the mission generation system creates the initial flight legs  318   c ,  318   d . From the initial flight leg  318   c , the mission generation system  100  generates initial flight leg  318   b  by drawing a line at the flight angle  316  a distance of the leg spacing  312  from the initial flight leg  318   c  (i.e., a distance perpendicular to the initial flight leg  318   c ). 
     Following this pattern, the mission generation system  100  can create the initial flight legs  318   a - 318   n . In particular, the mission generation system  100  can generate the initial flight legs  318   a - 318   n  such that the initial flight legs  318   a - 318   n  are oriented in the direction of the flight angle  316  and separated by the leg spacing  312 . 
     As mentioned previously, however, in one or more embodiments the mission generation system  100  creates flight legs that stay within the mission boundary  304 .  FIG. 3C  illustrates generation of flight legs within the mission boundary  304  in accordance with one or more embodiments. In particular, the mission generation system  100  determines that portions of the initial flight legs  318   e - 318   n  fall outside of the mission boundary  304 . Accordingly, the mission generation system  100  discards the portions of the initial flight legs  318   e - 318   n  that fall outside of the mission boundary  304 . Moreover, the mission generation system  100  identifies the portions of the initial flight legs  318   a - 318   n  that fall within the mission boundary  304  as flight legs  320 . 
     As illustrated in  FIG. 3C , upon identifying flight legs, in one or more embodiments the mission generation system  100  can also identify vertices of a mission boundary. In particular, with regard to  FIG. 3C , the mission generation system  100  identifies mission boundary corners  322  and flight leg endpoints  324 . Specifically, the mission generation system  100  identifies the flight leg endpoints  324  by determining the intersection of the flight legs  320  and the mission boundary  304 . Moreover, the mission generation system  100  identifies the mission boundary corners  322  by identifying ends of edges of the mission boundary  304 . 
     Although  FIGS. 3A-3C  illustrate a particular method for generating flight legs, it will be appreciated that other embodiments of the mission generation system  100  can generate flight legs utilizing other approaches. For example, rather than utilizing a centroid, one or more embodiments select an alternative starting point for generating flight legs. For example, in one or more embodiments, the mission generation system  100  can identify the longest edge of the mission boundary  304  and generate flight legs starting at the longest edge. In particular, the mission generation system  100  can start at the longest edge and create a flight leg that is a leg spacing (or ½ a leg spacing, or some other distance) from the longest edge at the flight angle. 
     In addition, although  FIGS. 3B-3C  illustrate a particular flight angle (i.e., a horizontal flight angle of zero), it will be appreciated that the mission generation system  100  can utilize any flight angle (or multiple flight angles). Indeed, as described previously, in one or more embodiments the mission generation system  100  selects the flight angle  316  in order to reduce (i.e., minimize) flight time. For example, in one or more embodiments, the mission generation system  100  selects the flight angle  316  such that the number of flight legs is minimized. In other embodiments, the mission generation system  100  selects the flight angle such that the length of flight legs is maximized. Specifically, in one or more embodiments, the mission generation system  100  generates initial flight legs at a variety of flight angles and selects final flight legs based on the particular flight angle that minimizes flight time (e.g., maximizes the length of flight legs or minimizes the number of flight legs). 
     Moreover, although  FIGS. 3B-3C  illustrate a particular leg spacing, it will be appreciated that the mission generation system can utilize any leg spacing (or multiple different leg spacings). Indeed, as described previously, in one or more embodiments the mission generation system  100  selects the leg spacing  312  based on one or more characteristics of a UAV. For instance, with regard to the embodiment of  FIGS. 3B-3C , the mission generation system  100  selects the leg spacing  312  based on a desired resolution (e.g., 1 pixel every 10 cm) and overlap (e.g., side overlap of 50%) of aerial images. Accordingly, the mission generation system  100  selects the leg spacing  312  based on the resolution and lens angle of the camera affixed to the UAV. 
     Upon identifying vertices, in one or more embodiments, the mission generation system  100  identifies one or more connections between vertices. In particular, in one or more embodiments, the mission generation system  100  identifies shortest connections between endpoints. For example,  FIG. 4  illustrates identifying shortest connections in accordance with one or more embodiments. In particular,  FIG. 4  illustrates the mission boundary  304 , a first endpoint  402 , a second endpoint  404 , a third endpoint  406 , and a fourth endpoint  408 . 
     The mission generation system  100  can identify two endpoints and calculate a shortest connection between the two endpoints subject to the condition that the shortest connection remains within the mission boundary. For example, as illustrated in  FIG. 4 , the mission generation system  100  can identify a shortest connection  410  between the first endpoint  402  and the second endpoint  404 . Specifically, because the first endpoint  402  and the second endpoint  404  are in a line of sight, the shortest connection  410  is a straight line between the first endpoint  402  and the second endpoint  404 . 
     In addition, the mission generation system  100  can identify a shortest connection between endpoints, even where the endpoints do not share a common line of sight. For example, a straight line between the first endpoint  402  and the third endpoint  406  would cross outside of the mission boundary  304 . Accordingly, the mission generation system  100  identifies a shortest connection  412  between the first endpoint  402  and the third endpoint  406  utilizing the mission boundary corners  420 ,  422 . In particular, the mission generation system  100  identifies the shortest connection  412  by finding the shortest distance between the first endpoint  402  and the first mission boundary corner  420 , finding the shortest distance between the first mission boundary corner  420  and the second mission boundary corner  422 , and then finding the shortest distance between the second mission boundary corner  422  and the third endpoint  406 . In this manner, the mission generation system can calculate connections between vertices that are not in line of sight with one another using intermediate vertices such that the total flight distance is minimized. 
     In addition, the mission generation system  100  can also identify a shortest connection between endpoints that lie on the mission boundary  304 . For example, the first endpoint  402  and the fourth endpoint  408  are adjacent endpoints on the mission boundary  304 . The mission generation system  100  can identify a shortest connection  414  between the first endpoint  402  and the fourth endpoint  408  as a line along the mission boundary  304 . 
     As mentioned previously, the mission generation system  100  can also generate a shortest connection database  430 . In particular, the shortest connection database  430  identifies the shortest connection between a plurality of endpoint pairs. More specifically the shortest connection database identifies the shortest connection between a plurality of endpoints pairs, with the condition that the shortest connection between each endpoint pair remains within the mission boundary  304 . Thus, for example, the shortest connection database  430  identifies the shortest connection  412  between the first endpoint  402  and the third endpoint  406  and the corresponding length of the shortest connection  412 . 
     In one or more embodiments, the mission generation system  100  calculates shortest connections between vertices utilizing a Floyd-Warshall algorithm. A Floyd-Warhsall algorithm can generate the lengths of the shortest paths between all pairs of vertices. Specifically, in one or more embodiments, the mission generation system  100  defines vertices as:
 
V=E∪C
 
where V is a set of vertices, E is a set of flight leg endpoints, and C is a set of corners of a mission boundary.
 
     In one or more embodiments, the mission generation system constructs a visibility graph, G from V. A visibility graph is a graph comprising edges between vertices that reside within a mission boundary. Accordingly, the visibility graph G comprises all edges between vertices, V, that fall within a mission boundary. In one or more embodiments, the mission generation system  100  executes a Floyd-Marshall algorithm with regard to G. The Floyd-Marshall algorithm produces the shortest path between any two vertices (e.g., endpoints) in polynomial time. 
     Because endpoints reflect the ends of flight legs, in one or more embodiments, the shortest connection database  430  reflects not only the shortest connection between endpoints, but also the shortest connect between flight legs. Accordingly, utilizing the methods described above, the mission generation system  100  can identify the shortest path between any pair of flight legs. The mission generation system  100  can utilize the shortest connections between flight legs to identify a single pathway (e.g., a flight path) that minimizes the total length. 
     As mentioned above, the mission generation system  100  can connect flight legs into a flight path utilizing a variety of algorithms. In particular, in one or more embodiments, the mission generation system  100  utilizes a nearest-neighbor linking algorithm. In one or more embodiments, a nearest-neighbor linking algorithm builds a flight path by starting at an endpoint of a flight leg, going through that flight leg, and proceeding to the nearest unvisited endpoint. In one or more embodiments, the mission generation system  100  runs the nearest neighbor linking algorithm over all possible starting points and the run resulting in the smallest total distance is chosen. 
     For example,  FIGS. 5A-5C  illustrate connecting flight legs utilizing a nearest-neighbor linking algorithm in accordance with one or more embodiments. In particular,  FIG. 5A  illustrates the mission boundary  304  with generated flight legs  502  and flight leg endpoints  504  within the mission boundary  304 . In applying a nearest-neighbor linking algorithm, the mission generation system  100  selects a first endpoint  504   a  as a starting point. The mission generation system  100  begins to define a flight path  506  by traversing the first flight leg  502   a  from the first endpoint  504   a  to the second endpoint  504   b , and adding the first flight leg  502   a  to the flight path  506 . 
     The mission generation system  100  then seeks the next nearest unvisited endpoint from the second endpoint  504   b . The mission generation system  100  determines that third endpoint  504   c  is the next nearest unvisited endpoint. In particular, with regard to the embodiment of  FIG. 5A , the mission generation system  100  references the shortest connection database  430  to determine the nearest unvisited endpoint from the second endpoint  504   b . The shortest connection database  430  indicates that the nearest endpoint from the second endpoint  504   b  is the third endpoint  504   c , which the flight path  506  has not yet visited. Accordingly, the mission generation system  100  adds the shortest connection from the second endpoint  504   b  to the third endpoint  504   c  to the flight path  506 . 
     The mission generation system  100  can proceed to add additional legs to the flight path following a similar pattern. For instance the mission generation system  100  proceeds to the third endpoint  504   c  and goes through the second flight leg  502   b  to the fourth endpoint  504   d . The mission generation system  100  adds the second flight leg  502   b  to the flight path  506  and searches for the nearest unvisited endpoint from the fourth endpoint  504   d.    
       FIG. 5B  illustrates the flight path  506  after the mission generation system  100  has proceeded to endpoint  504   n . At endpoint  504   n , the mission generation system  100  again searches for the next nearest, unvisited endpoint. Notably, there is no unvisited endpoint in the near vicinity of endpoint  504   n . However, the mission generation system  100  can identify that the next nearest unvisited endpoint is  504   o , which is not within the line of sight of endpoint  504   n . Utilizing identified vertices, the mission generation system  100  can identify the shortest connection between the endpoint  504   n  and the endpoint  504   o  (e.g., by referencing the shortest connection database  430 ). Moreover, the mission generation system  100  can add the shortest connection between endpoints  504   n  and  504   o  to the flight path  506 , as illustrated in  FIG. 5B . 
     Upon proceeding to the endpoint  504   o , the mission generation system  100  can add the flight leg  502   n  to the flight path, proceed to the next endpoint, locate the next nearest unvisited endpoint, and continue until no additional unvisited endpoints remain. For example,  FIG. 5C  illustrates the flight path  506  after adding all flight legs such that no additional unvisited endpoints remain. In this manner, the mission generation system  100  can utilize a nearest-neighbor linking algorithm to combine flight legs into a flight path. 
     Notably, the flight path  506  created utilizing the nearest-neighbor linking algorithm remains within the mission boundary  304 . Moreover, the flight path  506  traverses the target site while maintaining a particular leg spacing between legs. 
     As mentioned above, in one or more embodiments, the mission generation system  100  utilizes the nearest-neighbor linking algorithm to generate a plurality of flight paths based on different starting points and then selects a final flight path from the plurality of flight paths (e.g., the shortest flight path). For example, the mission generation system  100  can generate a second flight path that starts at the second endpoint  504   b  (rather than at the first endpoint  504   a ). The mission generation system  100  can compare the length of the second flight path and the length of the previously generated flight path and select the shortest flight path resulting from the comparison as the final flight path. In one or more embodiments, the mission generation system  100  calculates a flight path for every endpoint and selects the shortest resulting flight path. 
     In addition to the nearest-neighbor linking algorithm, the mission generation system  100  can also utilize a cheapest-edge linking algorithm to build a flight path. In particular, the mission generation system can generate a flight path by adding flight legs to the flight path and repeatedly adding the smallest unpicked connection that does not result in a cycle and does not create a vertex with three paths. 
     For example,  FIGS. 6A-6C  illustrate generating a flight path utilizing a cheapest-edge linking algorithm in accordance with one or more embodiments. In particular,  FIG. 6A  illustrates a mission boundary  600  encompassing a UAV flight area  602 . Moreover,  FIG. 6A  illustrates endpoints  604  corresponding to a plurality of flight legs generated by the mission generation system  100 . 
     In applying the cheapest-edge linking algorithm, the illustrated embodiment of the mission generation system  100  first adds each of the generated flight legs to a flight path  606 . As illustrated, however, adding the flight legs to the flight path  606 , leaves a series of unconnected flight legs. The mission generation system  100  connects flight legs utilizing the cheapest-edge linking algorithm by identifying the smallest unpicked edge that satisfies two conditions: (1) the unpicked edge, if added to the flight path, cannot result in a cycle (i.e., a closed loop) and (2) the unpicked edge, if added to the flight path, cannot result in a three-degree vertex (i.e., a vertex with three routes leading into our out of the vertex). Thus, with regard to the embodiment of  FIG. 6A  the mission generation system  100  identifies a first connection  610  as the smallest unpicked edge that satisfies both conditions. 
     In one or more embodiments, the mission generation system  100  identifies the first connection  610  by referencing a shortest connection database (e.g., the shortest connection database  430 ). For instance, the mission generation system  100  can identify the shortest connection from the database and determine whether the shortest connection satisfies the two conditions enumerated above. If the shortest connection does not satisfy the two conditions, the mission generation system  100  can proceed to the next shortest connection until finding the shortest available connection that satisfies the two conditions. For example, with regard to  FIG. 6A  the mission generation system  100  identifies the first connection  610  as the shortest connection in a shortest connection database. 
     As illustrated in  FIG. 6A , upon identifying the first connection  610 , the mission generation system  100  searches for the next shortest connection that does not result in a cycle or a three-degree vertex. Notably, however, because the mission generation system  100  added the first connection  610  to the flight path  606 , a number of connections fail to satisfy the requisite conditions. For instance, the mission generation system  100  cannot add the unused connection  612  to the flight path  606  because adding the unused connection  612  would create a loop. Similarly, the mission generation system  100  cannot add the second unused connection  614  because adding the second unused connection  614  to the flight path would result in a three-degree vertex (i.e., endpoint  604   a  would have three paths). 
     As illustrated, however, the mission generation system  100  ultimately identifies a second connection  620 . In particular, the mission generation system  100  identifies the second connection  620  as the next smallest unpicked connection that does not result in a cycle or a three-degree vertex. Accordingly, the mission generation system  100  adds the second connection  620  to the flight path  606 . Moreover, by adding the second connection  620 , the third unused connection  622  and the fourth unused connection  624  can no longer satisfy the pertinent criteria (i.e., the third unused connection  622  would create a loop and the fourth unused connection  624  would create a three-degree vertex). 
     The mission generation system  100  continues this pattern by identifying the next smallest unpicked connection that does not result in a cycle or a three-degree vertex (i.e., a third connection  630 ) and adding the next smallest connection to the flight path  606 . In this manner, the cheapest-edge linking algorithm connects the flight legs and builds the flight path  606 . After connecting a number of flight legs, however, the number of connections that can satisfy the pertinent criteria begin to dwindle. For example  FIG. 6B  illustrates the mission boundary  600 , the UAV flight area  602 , and the flight path  606  after adding a number of connections to the flight path  606 . In particular, the mission generation system  100  has added connections to the flight path  606  until only two endpoints  604   x  and  604   y  remain unconnected. 
     In applying the cheapest-edge linking algorithm, the mission generation system  100  identifies the next shortest connection that does not create a loop or a three-degree vertex. Unlike the previous connections, the connections immediately adjacent to endpoints  604   x  and  604   y  fail to satisfy these criteria. For instance a fifth unused connection  640  would both create a loop and create a three-degree vertex. Accordingly, the mission generation system  100  access a shortest connection database and identifies the shortest unused connection that satisfies the criteria. As illustrated in  FIG. 6C , by this process, the mission generation system  100  identifies a final shortest connection  650 . The final shortest connection  650  does not create a cycle between endpoints and does not create a three-degree vertex. Rather, as illustrated in  FIG. 6C , the cheapest-edge linking algorithm generates the flight path  606  that joins all flight legs. 
     In one or more embodiments, the cheapest-edge linking algorithm ends when the algorithm connects all available endpoints. Similarly, in one or more embodiments, the cheapest-edge linking algorithm continues until the number of segments in the flight path reaches a certain threshold. For example, in one or more embodiments, the cheapest-edge linking algorithm terminates upon determining that the number of segments in the flight path exceeds the total number of flight legs, times two, minus 1. 
     More specifically, in one or more embodiments, the mission generation system  100  applies the following pseudo-code in implementing the cheapest-edge linking algorithm: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 Add all flight legs as segments to the flight path; 
               
               
                 Start repeat; 
               
               
                   Pick the smallest unpicked connection that does not result in a cycle 
               
               
                   or three-degree vertex; 
               
               
                   Add the connection as a segment to the flight path; 
               
               
                   If S &gt; ((L * 2) − 1), then end repeat (where S is the number of 
               
               
                   segments in the flight path and L is the number of flight legs); 
               
               
                   If else, repeat; 
               
               
                 End. 
               
               
                   
               
            
           
         
       
     
     In addition to the cheapest-edge linking algorithm, the mission generation system  100  can also utilize a modified Christofides linking algorithm to generate a flight path from flight legs. A Christofides linking algorithm is an algorithm that finds a near-optimal solution to certain routing problems. In particular, with regard to applicable problems, the Christofides linking algorithm can calculate a route within 150% of a minimum. A Christofides linking algorithm generally operates by creating a minimum spanning tree for a problem, forming a multigraph, forming a Eulerian circuit, and making the circuit Hamiltonian by skipping visited nodes. 
     In one or more embodiments, the mission generation system  100  modifies a traditional Christofides linking algorithm. In particular, the mission generation system  100  applies a Christofides linking algorithm while adding a condition that the minimum spanning tree contains all flight legs. In other words, the mission generation system  100  creates a minimum spanning tree which contains all the flight legs. The mission generation system  100  then applies a Christofides algorithm with the specified condition to generate a flight path. In particular, the modified Christofied algorithm described herein produces a flight path that is within 150% of the minimum route. 
     Although this approach may not produce the shortest possible route, the modified Christofides linking algorithm requires fewer computing resources than some alternative approaches. Accordingly, the mission generation system  100  can utilize the modified Christofides linking algorithm to identify a near-optimal route without sacrificing exorbitant amounts of time or computing power. 
     Aside from the Christofides linking algorithm, in one or more embodiments, the mission generation system  100  can also apply a brute-force linking algorithm. A brute-force linking algorithm tests all permutations of available route configurations. Thus, the mission generation system  100  can apply a brute-force linking algorithm to test all possible connections between flight legs and identify the shortest resulting path. 
     For instance, in one or more embodiments, the mission generation system  100  utilizes the following pseudo-code to implement a brute-force algorithm: 
     
       
         
           
               
             
               
                   
               
             
            
               
                 Let two endpoints of any flight leg aL be aL 0  and aL 1 ; 
               
               
                 Let n be the number of flight legs; 
               
               
                 Let A = {0,1}, D = A n ; 
               
               
                 Let inv(x) = 0 when x = 1, and let inv(x) = 1 when x = 0; 
               
               
                 For all n-tuple s ∈ D; 
               
               
                   For all possible permutations of flight legs; 
               
               
                     Find the configuration with total flight distance given by the 
               
               
                     distance from1L s[1]  to 1L inv(s[1])  to 2L s[2]  to 2L inv(s[2])  to . . . 
               
               
                     to nL s[n]  to nL inv(s[n]) ; 
               
               
                     End for; 
               
               
                   End for; 
               
               
                 Return the configuration with the minimum total flight distance; 
               
               
                 End. 
               
               
                   
               
            
           
         
       
     
     In some circumstances, a brute-force linking algorithm can require a significant amount of time and/or computing resources. For example, with regard to a mission boundary containing twenty flight legs, applying a brute-force linking algorithm can require a significant amount of time and processing power. Accordingly, in one or more embodiments, prior to applying a brute-force linking algorithm the mission generation system  100  compares the number of flight legs (or number of endpoints) to a pre-determined threshold (e.g., a flight leg threshold). 
     In particular, if the mission generation system  100  determines that the number of flight legs exceeds (or meets) the flight leg threshold (e.g., 9 flight legs or 10 flight legs), the mission generation system will not apply the brute-force linking algorithm. However, if the mission generation system  100  determines that the number of flight legs falls below the flight leg threshold, in one or more embodiments, the mission generation system will apply the brute-force linking algorithm. Accordingly, in one or more embodiments, the mission generation system  100  can select a particular algorithm based on the number of flight legs (or endpoints) applicable to a particular target site. 
     In addition to the number of flight legs, in one or more embodiments, the mission generation system  100  can select a particular algorithm for combining flight legs into a flight path based on one or more other characteristics of the target site. For example, in one or more embodiments, the mission generation system  100  can select a particular algorithm based on a shape of the mission boundary. 
     In particular, in one or more embodiments, the mission generation system  100  determines whether a mission boundary is concave or convex. For instance, if a mission boundary is convex, in one or more embodiments, the mission generation system  100  will apply a nearest-neighbor linking algorithm. Similarly, if a mission boundary is concave, in one or more embodiments the mission generation system  100  will apply a cheapest-edge linking algorithm and a nearest-neighbor linking algorithm. Moreover, in other embodiments, if a mission boundary is concave the mission generation system  100  will apply a cheapest-edge linking algorithm, a nearest-neighbor linking algorithm, and a Christofides linking algorithm (and select the shortest resulting flight path). 
     The mission generation system  100  can also select one or more algorithms based on one or more characteristics of a computing device. For instance, the mission generation system  100  can determine that a computing device has limited processing capabilities, and, in response, apply only a cheapest-edge linking algorithm, only a Christofides linking algorithm, or only a nearest-neighbor linking algorithm (or some other combination of algorithms). 
     Turning now to  FIGS. 7A-7E , additional detail will be provided regarding generating a mission plan and one or more user interfaces for generating and presenting a mission plan in accordance with one or more embodiments. For example,  FIG. 7A  illustrates a computing device  700  with a display screen  702  showing a user interface  704 . 
     The user interface  704  is configured to display a variety of user interface elements, including, buttons, check-boxes, menus, drop-down menus, image elements, video elements, data display elements, and other elements. For instance, as shown, the user interface  704  contains a UAV selection element  706  and image display area  708 . Moreover, the image display area  708  displays an aerial image comprising a target site  710 . 
     The UAV selection element  706  illustrated in  FIG. 7A  enables a user to provide user input of a UAV for utilization by the mission generation system  100 . Indeed, as mentioned previously, the mission generation system  100  can perform a variety of functions based on one or more characteristics of a UAV. Accordingly, in one or more embodiments, the mission generation system  100  can modify its functions based on the user input of a UAV via the UAV selection element  706 . For instance, the mission generation system  100  can modify a leg spacing or an altitude based on one or more characteristics of the UAV provided via the UAV selection element  706 . 
     The image display area  708  is configured to provide a variety of maps, digital aerial images, or other images for display. For instance, as discussed previously, in one or more embodiments the mission generation system  100  can identify a mission boundary based on one or more aerial images. The image display area  708  can display one or more aerial images utilized by the mission generation system  100  to identify one or more mission boundaries. 
     For example, the mission generation system  100  can suggest a mission boundary utilizing on one or more images within the image display area  708 . Specifically, with regard to  FIG. 7A , the mission generation system  100  suggests a portion of the mission boundary based on the location of a road  712  contained in the aerial images displayed via the image display area  708 . In other embodiments, the mission generation system  100  can identify mission boundaries based on fences, power lines, paths, structures, ground control points, survey markers, or other features of an image. 
     Moreover, in one or more embodiments, the user can provide user input with regard to the image display area  708 . For example, as illustrated in  FIG. 7B , the mission generation system  100  can identify a mission boundary  720  based on user input with regard to the image display area  708 . In particular, a user can provide user input by selecting locations of the image display area  708  corresponding to locations represented in images shown on the image display area  708 . In particular, with regard to  FIG. 7B , the mission generation system  100  detects user input of eight corners  722   a - 722   h  of a polygon. The mission generation system  100  generates the mission boundary  720  based on the user input of the eight corners  722   a - 722   h.    
     As mentioned previously, the mission generation system  100  can identify mission boundary corners and/or edges based on digital flight area information. Thus, with regard to  FIG. 7B , the eight corners  722   a - 722   h  and corresponding polygon edges can correspond to digital flight area information. For instance, in one or more embodiments, the mission generation system  100  can fit or “snap” edges or corners based on digital flight area information. For example, with regard to  FIG. 7B , the mission generation system can snap the corner  722   b  to a property corner corresponding to the target site  710  based on digital property boundary information. In this manner, the mission generation system  100  can generate the mission boundary  720  based on digital flight area information, 
     As mentioned with regard to  FIG. 7A , in one or more embodiments, the mission generation system  100  can receive user input identifying a particular UAV (i.e., via the UAV selection element  706 ) and modify its operation based on the identified UAV. For example,  FIG. 7B  illustrates the UAV selection element  706  upon selection of a UAV (i.e., the UAV designated Skycatch EVO 2). 
     Moreover,  FIG. 7B  illustrates a mission statistics element  724 . The mission statistics element  724  can display various statistics pertinent to a mission plan. For instance, based on the selected UAV (i.e., selected via the UAV selection element  706 ), the mission generation system  100  can generate and display certain UAV characteristics pertinent to a mission via the mission statistics element  724 . For instance,  FIG. 7B  illustrates a ground speed (i.e. 6 m/s) calculated based on the selected UAV. 
     In addition,  FIG. 7B  also shows the user interface  704  with a resolution element  726 . The resolution element  726  permits a user to provide user input of a desired resolution of one or more images captured by a UAV. For instance, with regard to the illustrated embodiment, the user provides input indicating a resolution of 5 cm (i.e., 1 pixel per 5 cm). 
     As mentioned previously, the mission generation system  100  can utilize the selected resolution to modify its operations. For instance, the mission generation system  100  can utilize one or more characteristics of a UAV (e.g., based on the information received via the UAV selection element  706 ) and a desired resolution (e.g., based on the information provided via the resolution element  726 ) to calculate a UAV altitude or leg spacing. For instance, with regard to  FIG. 7B , the mission generation system identifies the camera affixed to the Skycatch EVO 2, identifies a camera resolution associated with the camera, and identifies the desired resolution of the resulting images. Based on the camera resolution and the desired resolution of the resulting images, the mission generation system  100  determines a flight altitude of 60 m (as illustrated in the mission statistics element  724 ). 
     As shown in  FIG. 7B , the user interface  704  also contains a location element  728 . The location element  728  can receive user input of one or more locations. For example, a user can input an address, city, state, zip code, descriptor, title, or other location identifier via the location element  728 . The mission generation system  100  can identify a location based on the information provided via the location element  728  and adjust the image display area  708  to display the identified location. For instance, upon receiving user input of an address via the location element  728 , the mission generation system can modify the image display area  708  to display one or more aerial images of the location corresponding to the address. 
     In addition to the location element  728 , the mission generation system  100  can also receive user input of a location based on user interaction with the image display area  708 . For example, a user can select and drag within the image display area  708 , and the image display area  708  will display aerial images of locations corresponding to the direction of the drag movement. Similarly, the mission generation system  100  can modify aerial images displayed via the image display area  708  so as to zoom in and out, rotate, or otherwise modify the image corresponding to one or more locations. 
       FIG. 7C  illustrates the user interface  704  with the image display area  708  displaying a mission plan  730  generated in accordance with one or more embodiments. As discussed previously, in one or more embodiments, the mission generation system can generate flight legs, combine flight legs to build a flight path within a mission boundary, and utilize a flight path to generate a mission plan. The embodiment of the mission generation system  100  illustrated in  FIG. 7C  utilizes similar methods to identify a flight path. For instance, the mission generation system  100  identifies a leg spacing based on one or more characteristics of the selected UAV (i.e., via the UAV selection element  706 ) and based on the selected resolution (i.e., via the resolution element  726 ). The mission generation system  100  also identifies a flight angle based on user input (i.e., based on user input of a flight angle). Utilizing the flight angle and leg spacing, the mission generation system  100  generates flight legs. Moreover, the mission generation system  100  detects that the mission boundary  720  is concave, and therefore applies a nearest-neighbor linking algorithm and a cheapest-edge linking algorithm to the generated flight legs. Furthermore, the mission generation system  100  identifies a shortest flight path based on the flight paths resulting from the nearest-neighbor linking algorithm and the cheapest-edge linking algorithm. 
     As illustrated in  FIG. 7C , the mission generation system  100  also generates a mission plan from the flight path. For instance, the mission generation system  100  detects elevation data and generates altitude information for the mission plan. Specifically, in  FIG. 7C , the mission generation system  100  accesses elevation data from NASA&#39;s Shuttle Radar Topography Mission (SRTM) corresponding to the location displayed in the image display area  708 . As described previously, in other embodiments, the mission generation system  100  can generate elevation data utilizing a plurality of aerial images. 
     The mission generation system  100  can also provide elevation data for display. Indeed, as illustrated in  FIG. 7C , the mission generation system  100  provides, via the user interface  704 , the elevation data element  732 . The elevation data element  732  provides a user with the elevation of a location displayed via the image display area  708 . In particular, the elevation data element  732  can provide the elevation of a particular location in response to a user moving a cursor (or finger in the case of a tablet or touchscreen device) to a position on the image display area  708  corresponding to the particular location. Thus, as shown, the elevation data element  732  shows the elevation (i.e., 1303 m) associated with a particular position indicated by a user (i.e., coordinate (40.87, −111.91) within a local coordinate scheme). 
     As described previously, the mission generation system  100  can utilize elevation data to generate altitude information for a mission plan. In particular, with regard to  FIG. 7C , the mission generation system  100  generates altitude data for the mission plan utilizing elevation data corresponding to the target site. In particular, the mission generation system  100  generates altitude data such that a UAV will maintain an altitude of approximately 60 m as it traverses the UAV flight area. More specifically, the mission generation system  100  generates waypoints  1 - 36  in the mission plan (i.e., along the flight path) to maintain a desired altitude. In particular, the mission generation system  100  defines an altitude corresponding to each of the waypoints  1 - 36  to obtain a desired altitude. 
     Moreover, the mission generation system  100  can provide the altitude of various waypoints for display. In particular,  FIG. 7C  illustrates the user interface  704  with the elevation and terrain element  734 . The elevation and terrain element  734  can display altitude data and corresponding elevation data of a mission plan along one or more flight paths. For example, the elevation and terrain element  734  displays the altitude of each waypoint and the elevation data of the target site corresponding to each waypoint. Similarly, the elevation and terrain element  734  displays the altitude data of the mission plan between waypoints with the corresponding elevation data of the target site. 
     Although  FIG. 7C  illustrates waypoints at endpoints, it will be appreciated that the mission generation system  100  can add waypoints at a variety of locations to obtain a desired altitude. For example, if a target site contains an abrupt jump in elevation, the mission generation system  100  can add one or more waypoints (e.g., add a waypoint within a flight leg) corresponding to the location of the jump in elevation of the target site. Thus, the mission generation system  100  can add waypoints at flight leg endpoints, in the middle of a flight leg, or at any other location. 
     As mentioned, the mission generation system  100  can also consider battery life, range, environmental factors, or other characteristics of a UAV in generating a mission plan. Thus, with regard to  FIG. 7C , the mission generation system  100  has divided the mission plan into two flight paths (i.e., one flight path before waypoint  18 , and another flight path after waypoint  19 ). Specifically, the mission generation system  100  has divided the mission plan into two flight paths based on the estimated battery life of the selected UAV (i.e., Skycatch EVO 2). In particular, after the UAV reaches waypoint  18 , flight the mission generation system  100  can direct the UAV to return to a docking station to recharge a battery (or exchange batteries). Moreover, upon recharging the battery (or exchanging batteries) the mission generation system  100  can direct the UAV to return to waypoint  19 . 
     The mission generation system  100  can also account for the location of a docking station in generating a mission plan (or generating a flight path). For example, in one or more embodiments, the mission generation system  100  can identify a location of a docking station and modify a mission plan to reduce flight time. For example, with regard to  FIG. 7C  the mission generation system determines that the docking station will be located in close proximity to waypoint  18 . Accordingly, the mission generation system  100  generates two flight paths, and divides the flight paths at waypoint  18  (i.e., a location corresponding to the location of the docking station). 
     As just mentioned, in one or more embodiments, the mission generation system  100  will split flight paths in generating a mission plan. In particular, in one or more embodiments, the mission generation system  100  will split flight paths based on user input. For example,  FIG. 7C  illustrates an auto-split element  738 . The auto-split element  738  can toggle “on” or “off” based on user interaction. When the auto-split element  738  is in the “on” position, the mission generation system  100  can divide flight paths based on one or more characteristics of the UAV (e.g., battery life, flight range), environmental elements, the location of a docking station, or other features. When the auto-split element  738  is in the “off” position, the mission generation system  100  will not automatically divide flight paths. 
     Similarly, in one or more embodiments, the mission generation system  100  also permits a user to control whether the mission generation system  100  will adjust UAV altitude based on elevation data of the target site. In particular,  FIG. 7C  illustrates an auto-adjust terrain element  739 . Based on user input with the auto-adjust terrain element  739 , the mission generation system  100  can toggle whether it will modify UAV altitude in the mission plan based on elevation data. 
     As mentioned previously, in one or more embodiments, the mission generation system  100  identifies obstacles and modifies a mission plan based on the identified obstacles. For example,  FIG. 7C  illustrates an add obstacle element  736 . The add obstacle element  736  enables a user to provide user input of an obstacle for utilization by the mission generation system  100 . Specifically, upon user interaction with the add obstacle element  736 , the mission generation system  100  can receive user input of an obstacle via the image display area  708 . 
     For example,  FIG. 7D  illustrates an obstacle  740  identified based on user input via the image display area  708 . In particular, the mission generation system  100  identifies the obstacle  740  based on user selection of corners associated with the obstacle  740 . Although illustrated as a polygon, the obstacle  740  can comprise any shape. 
     In addition to identifying a location and/or shape of an obstacle, the mission generation system  100  can also identify other characteristics of an obstacle. In particular, the mission generation system  100  can identify an obstacle elevation. For example, in one or more embodiments, the mission generation system  100  can receive user input of one or more obstacle elevations. In other embodiments, the mission generation system  100  can identify an obstacle elevation based on elevation data. 
     In addition to obstacle elevation, the mission generation system  100  can also identify an obstacle buffer spacing. In particular, the mission generation system  100  can identify an obstacle buffer spacing that describes a minimum distance from the obstacle that a UAV will maintain during flight. In one or more embodiments, the mission generation system  100  can identify an obstacle buffer spacing based on user input (e.g., user input that a UAV must maintain a distance of 20 m from an obstacle). In other embodiments, the mission generation system  100  can identify an obstacle buffer spacing based on the type of obstacle (e.g., a first distance for a building, a second distance for power lines, a third distance for trees, a fourth distance for elevated terrain, etc.). 
     Moreover, upon identifying an obstacle, in one or more embodiments, the mission generation system  100  modifies the mission plan based on the identified obstacle. For example,  FIG. 7E  illustrates a modified mission plan based on the obstacle  740 . In particular, the mission generation system  100  has added new waypoints  12 - 15 . In particular, the first new waypoint  12  is located along a flight path prior to the obstacle  740  at a first altitude (e.g., an altitude below the obstacle elevation). The second new waypoint  13  is located along the flight path prior to the obstacle  740  at a second altitude higher than the first altitude (e.g., an altitude above the obstacle  740 ). The third new waypoint  14  is located along the flight path after the obstacle  740  at a third elevation greater than the first elevation (e.g., an altitude above the obstacle  740 ). The fourth new waypoint  15  is located along the flight path after the obstacle  740  at a fourth elevation lower than the third altitude (e.g., an altitude below the obstacle  740 ). By adding the new waypoints  12 - 15 , the mission generation system  100  can generate a mission plan that enables a UAV to fly over the obstacle  740 . 
     In addition to flying over an obstacle, the mission generation system  100  can modify a mission plan to avert an obstacle in a variety of other ways. For example, in one or more embodiments, the mission generation system  100  can modify a mission plan to circumvent an obstacle. For example, rather than adding new waypoints  12 - 15  with altitude information to fly above the obstacle  740 , the mission generation system  100  can add new waypoints at locations around the obstacle (e.g., add way points corresponding to the corners of the obstacle  740 ). 
     In addition, in one or more embodiments, the mission generation system  100  can modify a mission plan to circumvent an obstacle, while still capturing aerial images of the obstacle. For instance, upon identifying an obstacle, the mission generation system  100  can generate a series of waypoints that circumvent the obstacle at different altitudes. For example, the mission generation system  100  can identify a structure and generate a series of waypoints that orbit the structure at a plurality of altitudes, each altitude separated by a vertical spacing (e.g., altitudes of 60 m, 100 m, 140 m, 180 m, etc.). Moreover, the mission generation system  100  can modify an orientation of a camera affixed to the UAV such that the camera captures aerial images of the structure (e.g., the side of a building), as the UAV orbits at the plurality of altitudes. In this manner, the mission generation system  100  can generate a mission plan that circumvents an obstacle while capturing aerial images of the obstacle. 
     In addition, the mission generation system can select a vertical spacing based on one or more characteristics of an obstacle and/or one or more characteristics of a UAV. For example, the mission generation system  100  can select a vertical spacing based on an elevation (or height) of a structure, based on a shape of a structure, based on a resolution of a camera affixed to a UAV, or based on other factors. 
     Moreover, although  FIGS. 7C-7E  illustrate an obstacle identified via user input, it will be appreciated that the mission generation system  100  can identify obstacles without user input. For example, the mission generation system  100  can generate a three-dimensional model of a target site utilizing a plurality of images, and identify obstacles based on the three-dimensional model. 
       FIGS. 1-7E , the corresponding text, and the examples, provide a number of different systems and devices for generating a mission plan. In addition to the foregoing, one or more embodiments can also be described in terms of flowcharts comprising acts and steps in a method for accomplishing a particular result. For example,  FIGS. 8 and 9  illustrate flowcharts of exemplary methods in accordance with one or more embodiments. The methods described in relation to  FIGS. 8 and 9  may be performed with less or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts. 
       FIG. 8  illustrates a flowchart of one example method  800  of generating a mission plan in accordance with one or more embodiments. As illustrated, the method  800  may include the act  802  of identifying a mission boundary. In particular, the act  802  includes identifying a mission boundary defining a UAV flight area, the mission boundary encompassing a target site for capturing a plurality of aerial images by a UAV. 
     For instance, the act  802  may include accessing digital flight area information, the digital flight area information comprising at least one of: a digital flight zone, a digital property boundary, a digital aerial image, or a digital survey. Moreover, the act  802  may include transforming the digital flight area information to at least a portion of the mission boundary. 
     As illustrated in  FIG. 8 , the method  800  also includes an act  804  of generating flight legs. In particular, the act  804  may include generating, by at least one processor, flight legs for the UAV flight area, the flight legs being separated by a leg spacing based on one or more characteristics of the UAV, and each flight leg intersecting the mission boundary at two endpoints. For instance, in one or more embodiments, the one or more characteristics of the UAV comprises at least one of the following: a resolution of a camera affixed to the UAV, a resolution of images resulting from use of the camera, a lens angle of the camera, a width of images resulting from use of the camera, or a maximum flight altitude of the UAV. 
     In addition, the act  804  may also include generating each flight leg at a flight angle. Moreover, the act  804  can include calculating the flight angle applicable to the flight legs such that the angle maximizes an average length of the flight legs. 
     Moreover, as shown in  FIG. 8 , the method  800  also includes an act  806  of identifying flight vertices. In particular, the act  806  may include identifying flight vertices, the flight vertices comprising corners of the mission boundary and the endpoints for each of the flight legs. 
     In addition,  FIG. 8  illustrates that the method  800  also includes an act  808  of building a flight path utilizing the flight vertices. In particular, the act  808  includes building, by the at least one processor, a flight path by combining the flight legs utilizing the flight vertices, wherein the flight path does not extend beyond the mission boundary. 
     Moreover, the act  808  may also include determining whether the mission boundary is convex or concave. In addition, if the mission boundary is convex, the act  808  may include building a flight path by applying a first flight leg linking algorithm. Similarly, if the mission boundary is concave, the act  808  may include building a flight path by applying a second flight leg linking algorithm different from the first flight leg linking algorithm. More specifically, in one or more embodiments, the act  808  includes calculating a plurality of shortest connections by, for a plurality of pairs of the endpoints, calculating the shortest connection within the UAV flight area between each pair of endpoints. Moreover, in one or more embodiments, the first flight leg linking algorithm is a nearest-neighbor linking algorithm that utilizes the flight legs and the plurality of shortest connections to combine a second flight leg to an initial flight leg based on a determination that the second flight leg and the initial flight leg are closest in proximity. In addition, in one or more embodiments, the second flight leg linking algorithm is a cheapest-edge linking algorithm that utilizes the flight legs and the plurality of shortest connections to combine flight legs based on a determination of the shortest connection available between unused flight leg endpoints that does not create a cycle or a three-degree vertex. 
     Furthermore, the act  808  may also include determining that the number of flight legs falls below a predetermined flight leg threshold and, based on the determination that the number of flight legs falls below the predetermined flight leg threshold, applying a third algorithm different from the first algorithm and the second algorithm. For instance, in one or more embodiments, the third algorithm is a brute-force algorithm. 
     As shown in  FIG. 8 , the method  800  also includes the act  810  of generating a mission plan based on the flight path. In particular, the act  810  includes generating a mission plan based on the flight path, the mission plan comprising computer-executable instructions for causing the UAV to capture aerial images of the target site in accordance with the flight path. 
     In addition, in one or more embodiments, the act  810  includes identifying digital elevation data with regard to the target site. Furthermore, the act  810  can include defining UAV flight altitudes within the mission plan based on the digital elevation data and the one or more characteristics of the UAV. For instance, the act  810  can include capturing a plurality of aerial images of the target site utilizing a UAV, and calculating elevation data with regard to the target site based on the plurality of aerial images. Thus, the act  810  can include defining UAV flight altitude within the mission plan based on the elevation data and the one or more characteristics of the UAV. Moreover, the act  810  can include accessing a digital image, the digital image comprising elevation data transformed into RGB values within the digital image, and transforming a plurality of the RGB values into elevation data. 
     The act  810  can also include identifying an obstacle, the obstacle corresponding to an obstacle elevation. In addition, the act  810  can include creating a first waypoint at a first altitude, the first altitude lower than the obstacle elevation; creating a second waypoint at a second altitude, the second waypoint higher than the obstacle elevation; and adding the first waypoint and the second waypoint to the mission plan such that the first waypoint and the second waypoint correspond to a location of the obstacle. 
       FIG. 9  illustrates another flowchart of one example method  900  of generating a mission plan in accordance with one or more embodiments. As illustrated, the method  900  may include the act  902  of identifying a mission boundary. In particular, in one or more embodiments, the act  902  includes identifying a mission boundary defining a UAV flight area. 
     As illustrated in  FIG. 9 , the method  900  also includes the act  904  of generating flight legs. In particular, the act  902  may include generating, by at least one processor, flight legs for the UAV flight area, the flight legs contained within the UAV flight area. 
     Moreover,  FIG. 9  also shows that the method  900  includes the act  906  of determining whether the mission boundary is convex or concave. For instance, the act  906  may include determining that the mission boundary is convex. Additional, or alternatively, the act  906  may include determining that the mission boundary is concave. Similarly, the act  906  may include identifying a plurality of mission boundaries and determining whether each of the plurality of mission boundaries is convex or concave. In addition, the act  906  may also include identifying an inner mission boundary and an outer mission boundary. The act  906  may also include determining that the inner mission boundary is encompassed by the outer mission boundary. 
     As shown in  FIG. 9 , the method  900  also includes the act  908  of, if the mission boundary is convex, building a flight path by applying a first flight leg linking algorithm. For instance, the act  908  may include if the mission boundary is convex, building a flight path by applying a first flight leg linking algorithm to the flight legs. 
     For instance the act  908  may include applying a nearest-neighbor linking algorithm. More specifically, the act  908  may include identifying a starting point comprising a first endpoint corresponding to an initial flight leg, the initial flight leg having both the first endpoint and a second endpoint; identifying a third endpoint corresponding to a second flight leg, the third endpoint being closest to the second endpoint of the initial flight leg along a first shortest connection from the shortest connections within the permissible UAV flight area; and building the flight path by combining the first flight leg, the first shortest connection, and the second flight leg. 
     Furthermore the act  908  may also include, identifying a plurality of additional starting points, each additional starting point comprising a different endpoint; building a plurality of additional flight paths based on the plurality of additional starting points; and selecting the flight path based on a comparison between the length of the flight path and the length of each additional flight path. 
     Similarly, as illustrated in  FIG. 9 , the method  900  also includes the act  910  of, if the mission boundary is concave, building a flight path by applying a second flight leg linking algorithm. In particular, the act  910  may include if the mission boundary is concave, building a flight path by applying a second flight leg linking algorithm different from the first flight leg linking algorithm to the flight legs. 
     For instance, the second flight leg linking algorithm may include a cheapest-edge linking algorithm. Accordingly, in one or more embodiments, the act  910  includes adding each flight leg to the flight path; identifying the shortest connection from the calculated shortest connections within the UAV flight area between the plurality of the endpoints; adding the identified shortest connection to the flight path; identifying the next shortest connection from the remaining shortest connections within the permissible UAV flight area between a plurality of the endpoints; and based upon a determination that adding the next shortest connection to the flight path does not create a cycle and does not result in a three-degree vertex, adding the next shortest connection to the flight path. In one or more embodiments, each flight leg intersects the mission boundary at two endpoints. Moreover, the method  900  may also include calculating, utilizing a Floyd-Warshall algorithm, the shortest connections within the UAV flight area between a plurality of endpoints. 
     Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein. 
     Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media. 
     Non-transitory computer-readable storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. 
     A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media. 
     Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media (devices) at a computer system. Thus, it should be understood that non-transitory computer-readable storage media (devices) can be included in computer system components that also (or even primarily) utilize transmission media. 
     Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to turn the general purpose computer into a special purpose computer implementing elements of the disclosure. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices. 
     Embodiments of the present disclosure can also be implemented in cloud computing environments. In this description, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing can be employed in the marketplace to offer ubiquitous and convenient on-demand access to the shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly. 
     A cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth. In this description and in the claims, a “cloud-computing environment” is an environment in which cloud computing is employed. 
       FIG. 10  illustrates a block diagram of an exemplary computing device  1000  that may be configured to perform one or more of the processes described above. One will appreciate that the mission generation system may be implemented by one or more computing devices such as the computing device  1000 . As shown by  FIG. 10 , the computing device  1000  can comprise a processor  1002 , memory  1004 , a storage device  1006 , an I/O interface  1008 , and a communication interface  1010 , which may be communicatively coupled by way of a communication infrastructure  1012 . While an exemplary computing device  1000  is shown in  FIG. 10 , the components illustrated in  FIG. 10  are not intended to be limiting. Additional or alternative components may be used in other embodiments. Furthermore, in certain embodiments, the computing device  1000  can include fewer components than those shown in  FIG. 10 . Components of the computing device  1000  shown in  FIG. 10  will now be described in additional detail. 
     In particular embodiments, the processor  1002  includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, the processor  1002  may retrieve (or fetch) the instructions from an internal register, an internal cache, the memory  1004 , or the storage device  1006  and decode and execute them. In particular embodiments, the processor  1002  may include one or more internal caches for data, instructions, or addresses. As an example and not by way of limitation, the processor  1002  may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in the memory  1004  or the storage  1006 . 
     The memory  1004  may be used for storing data, metadata, and programs for execution by the processor(s). The memory  1004  may include one or more of volatile and non-volatile memories, such as Random Access Memory (“RAM”), Read Only Memory (“ROM”), a solid state disk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of data storage. The memory  1004  may be internal or distributed memory. 
     The storage device  1006  includes storage for storing data or instructions. As an example and not by way of limitation, the storage device  1006  can comprise a non-transitory storage medium described above. The storage device  1006  may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. The storage device  1006  may include removable or non-removable (or fixed) media, where appropriate. The storage device  1006  may be internal or external to the computing device  1000 . In particular embodiments, the storage device  1006  is non-volatile, solid-state memory. In other embodiments, the storage device  1006  includes read-only memory (ROM). Where appropriate, this ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. 
     The I/O interface  1008  allows a user to provide input to, receive output from, and otherwise transfer data to and receive data from the computing device  1000 . The I/O interface  1008  may include a mouse, a keypad or a keyboard, a touch screen, a camera, an optical scanner, network interface, modem, other known I/O devices or a combination of such I/O interfaces. The I/O interface  1008  may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, the I/O interface  1008  is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation. 
     The communication interface  1010  can include hardware, software, or both. In any event, the communication interface  1010  can provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device  1000  and one or more other computing devices or networks. As an example and not by way of limitation, the communication interface  1010  may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. 
     Additionally or alternatively, the communication interface  1010  may facilitate communications with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, the communication interface  1010  may facilitate communications with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination thereof 
     Additionally, the communication interface  1010  may facilitate communications various communication protocols. Examples of communication protocols that may be used include, but are not limited to, data transmission media, communications devices, Transmission Control Protocol (“TCP”), Internet Protocol (“IP”), File Transfer Protocol (“FTP”), Telnet, Hypertext Transfer Protocol (“HTTP”), Hypertext Transfer Protocol Secure (“HTTPS”), Session Initiation Protocol (“SIP”), Simple Object Access Protocol (“SOAP”), Extensible Mark-up Language (“XML”) and variations thereof, Simple Mail Transfer Protocol (“SMTP”), Real-Time Transport Protocol (“RTP”), User Datagram Protocol (“UDP”), Global System for Mobile Communications (“GSM”) technologies, Code Division Multiple Access (“CDMA”) technologies, Time Division Multiple Access (“TDMA”) technologies, Short Message Service (“SMS”), Multimedia Message Service (“MMS”), radio frequency (“RF”) signaling technologies, Long Term Evolution (“LTE”) technologies, wireless communication technologies, in-band and out-of-band signaling technologies, and other suitable communications networks and technologies. 
     The communication infrastructure  1012  may include hardware, software, or both that couples components of the computing device  1000  to each other. As an example and not by way of limitation, the communication infrastructure  1012  may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination thereof. 
     In the foregoing specification, the present disclosure has been described with reference to specific exemplary embodiments thereof. Various embodiments and aspects of the present disclosure(s) are described with reference to details discussed herein, and the accompanying drawings illustrate the various embodiments. The description above and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. 
     The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be performed with less or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts. The scope of the present application is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.