Patent Publication Number: US-2022230551-A1

Title: Unmanned Aircraft Vehicle State Awareness

Description:
TECHNICAL FIELD 
     The present disclosure is directed to unmanned flight systems and more particularly to hazard avoidance and risk analysis of unmanned flight missions and aircraft. 
     BACKGROUND OF THE INVENTION 
     Unmanned flight systems are becoming more and more prevalent. Both military and commercial entities are finding uses for unmanned flight. With unmanned flight comes risks, because when a hazard arises there is no onboard pilot to redirect an aircraft. Systems and methods are needed that help protect the aircraft itself, but also protect the innocent lives of civilians and non-combatants. 
     BRIEF SUMMARY OF THE INVENTION 
     One embodiment under the present disclosure can comprise a method of operating an unmanned vehicle comprising a vehicle management system (VMS). This method can comprise; receiving, by the VMS, a travel plan identifying one or more hazards along a travel route; receiving, by the VMS, statistical failure data related to one or more components comprising the unmanned vehicle; collecting, by the VMS, sensor data from one or more sensors comprising the unmanned vehicle, the sensor data related to operational capabilities of the unmanned vehicle; calculating, by the VMS, a health state assessment, the health state assessment comprising a probability of failure related to the one or more hazards and calculated with the statistical failure data and the sensor data; and comparing, by the VMS, the probability of failure to an accepted probability of failure. If the probability of failure is greater than the accepted probability of failure, activating a contingency maneuver, and if the probability of failure is less than the accepted probability of failure, continuing the travel plan. 
     Another embodiment under the present disclosure can comprise an unmanned aircraft comprising a vehicle management system (VMS). The unmanned aircraft can comprise: one or more components configured to assist in operating the unmanned aircraft; one or more sensors configured to detect real-time operational data of the unmanned aircraft; one or more processors comprising the VMS that are operable to receive statistical data regarding failure of the one or more components from a remote database, further operable to receive the real-time operational data from the one or more sensors, and further operable to access a flight plan comprising identified critical elements along the flight plan. The VMS is operable to, upon approaching a critical element, calculate a state health assessment, the state health assessment calculated by; calculating a component failure rate for each of the one or more components using the statistical data; analyzing the real-time operational data to determine if it mandates an adjustment to the component failure rates; adjusting the component failure rates if mandated by the real-time operational data; and multiplying the adjusted component failure rates to give a net failure probability rate. The VMS is further operable to compare the net failure probability rate to an accepted failure probability rate, and if the net failure probability rate is greater than the accepted failure probability rate, activating a contingency maneuver, and if the net failure probability rate is less than the accepted failure probability rate, continuing the flight plan. 
     Another embodiment under the present disclosure can comprise a method of operating an unmanned aircraft comprising a vehicle management system (VMS). This method can comprise: receiving, by the VMS, a rotor failure rate of one or more rotors comprising the unmanned aircraft; receiving, by the VMS, a battery failure rate of one or more batteries comprising the unmanned aircraft; receiving, by the VMS, real-time operational data from one or more sensors comprising the unmanned aircraft; receiving, by the VMS, a flight plan describing a route of travel and comprising one or more critical elements along the route of travel; controlling, by the VMS, the unmanned aircraft to takeoff and begin the flight plan. Upon nearing the one or more critical elements, further steps include, adjusting, by the VMS, the battery failure rate and the rotor failure rate using the real-time operational data to provide an adjusted battery rate and an adjusted rotor rate; multiplying, by the VMS, the adjusted battery rate and the adjusted rotor rate to yield a critical element failure rate; comparing, by the VMS, the critical element failure rate to an approved failure rate related to the one or more critical elements. If the critical element failure rate is greater than the approved failure rate, then a contingency action for the unmanned aircraft to perform is activated, and if the critical element failure rate is less than the approved failure rate, the unmanned aircraft continues along the flight plan. 
     The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter which form the subject of the claims of the invention. It should be appreciated by those skilled in the art that the conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims. The novel features which are believed to be characteristic of the invention, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which: 
         FIGS. 1A-1F  show schematic views of an unmanned aircraft under the present disclosure. 
         FIG. 2  shows a flow chart embodiment of a control system under the present disclosure. 
         FIG. 3  shows an aircraft embodiment special view under the present disclosure. 
         FIG. 4  shows a flow-chart of a possible method embodiment under the present disclosure. 
         FIG. 5  shows a flow-chart of a possible method embodiment under the present disclosure. 
         FIG. 6  shows a schematic of a possible aircraft embodiment under the present disclosure. 
         FIG. 7  shows a flow-chart of a possible method embodiment under the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to  FIGS. 1A-1F  in the drawings, various views of an aircraft embodiment  10  under the present disclosure can be seen. Aircraft  10  comprises an unmanned aircraft having a distributed thrust array including gimbal mounted propulsion systems operable for thrust vectoring are depicted.  FIGS. 1A, 1C, 1E  depict aircraft  10  in thrust-borne flight which may also be referred to as the vertical takeoff and landing or VTOL flight mode of aircraft  10 .  FIGS. 1B, 1D, 1F  depict aircraft  10  in wing-borne flight which may also be referred to as the forward or high-speed forward flight mode of aircraft  10 . In the illustrated embodiment, the airframe  12  of aircraft  10  includes wings  14   a ,  14   b  each having an airfoil cross-section that generates lift responsive to the forward airspeed of aircraft  10 . Wings  14   a ,  14   b  may be formed as single members or may be formed from multiple wing sections. Extending generally perpendicularly between wings  14   a ,  14   b  are two truss structures depicted as pylons  16   a ,  16   b . In other embodiments, more than two pylons may be present. Wings  14   a ,  14   b  and pylons  16   a ,  16   b  may be coupled together at the respective intersections using mechanical connections such as bolts, screws, rivets, adhesives and/or other suitable joining technique. Extending generally perpendicularly from wings  14   a ,  14   b  are landing gear depicted as tail members  18   a ,  18   b ,  18   c ,  18   d  that enable aircraft  10  to operate as a tail-sitting aircraft. In the illustrated embodiment, tail members  18   a ,  18   b ,  18   c ,  18   d  are fixed landing struts. In other embodiments, tail members  18   a ,  18   b ,  18   c ,  18   d  may include passively operated pneumatic landing struts or actively operated telescoping landing struts with or without wheels for ground maneuvers. Tail members  18   a ,  18   b ,  18   c ,  18   d  each include a control surface  20   a ,  20   b ,  20   c ,  20   d , respectively, that may be passive or active aerosurfaces that serve as vertical stabilizers and/or elevators during wing-borne flight and serve to enhance hover stability during thrust-borne flight. 
     In the illustrated embodiment, wings  14   a ,  14   b  and/or pylons  16   a ,  16   b  may contain one or more of electrical power sources depicted as batteries  22  in wing  14   a , as best seen in  FIG. 1A . Batteries  22  supply electrical power to flight control system  32 . In some embodiments, batteries  22  may be used to supply electrical power for the distributed thrust array of aircraft  10 . Wings  14   a ,  14   b  and/or pylons  16   a ,  16   b  also contain a communication network  24  that enables flight control system  32  to communicate with the distributed thrust array of aircraft  10 . 
     In the illustrated embodiment, the distributed thrust array includes four propulsion assemblies  26   a ,  26   b ,  26   c ,  26   d  that are independently operated and controlled by flight control system  32 . As illustrated, propulsion assemblies  26   a ,  26   b ,  26   c ,  26   d  are coupled to the outboard ends of wings  14   a ,  14  b. In other embodiments, propulsion assemblies  26   a ,  26   b ,  26   c ,  26   d  could have other configurations including close coupled configurations, high wing configurations, low wing configurations or other suitable configuration. In the illustrated embodiment, each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d  includes a housing  28   a ,  28   b ,  28   c ,  28   d , that contains components such as an electric motor, a gimbal, one or more actuators and an electronics node including, for example, batteries, controllers, sensors and other desired electronic equipment. Only electric motors  30   a ,  30   b  and electronics nodes  32   a ,  32   b  are visible in  FIG. 1A . The electric motors of each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d  are preferably operated responsive to electrical energy from the battery or batteries disposed with that housings, thereby forming a distributed electrically powered thrust array. Alternatively, or additionally, electrical power may be supplied to the electric motors and/or the batteries disposed with the housing from batteries  22  carried by airframe  12  via communications network  24 . In other embodiments, the propulsion assemblies may include internal combustion engines or hydraulic motors. 
     Each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d  includes a rotor assembly  34   a ,  34   b ,  34   c ,  34   d . Each rotor assembly  34   a ,  34   b ,  34   c ,  34   d  is directly or indirectly coupled to an output drive of a respective electrical motor  30   a ,  30   b ,  30   c ,  30   d  that rotates the rotor assembly  34   a ,  34   b ,  34   c ,  34   d  in a rotational plane to generate thrust for aircraft  10 . In the illustrated embodiment, rotor assemblies  34   a ,  34   b ,  34   c ,  34   d  each include three rotor blades having a fixed pitch. In other embodiments, the rotor assemblies could have other numbers of rotor blades both less than and greater than three. Alternatively, or additionally, the rotor assemblies could have variable pitch rotor blades with collective and/or cyclic pitch control. Each electrical motor  30   a ,  30   b ,  30   c ,  30   d  is paired with a rotor assembly  34   a ,  34   b ,  34   c ,  34   d , for example electrical motor  30   a  and rotor assembly  34   a , to form a propulsion system  36   a ,  36   b ,  36   c ,  36   d . As described herein, each propulsion system  36   a ,  36   b ,  36   c ,  36   d  may have a single-axis or a two-axis tilting degree of freedom relative to housings  28   a ,  28   b ,  28   c ,  28   d  and thus airframe  12  such that propulsion systems  36   a ,  36   b ,  36   c ,  36   d  are operable for thrust vectoring. 
     Aircraft  10  may operate as a transport aircraft for a pod assembly  50  that is fixed to or selectively attachable to and detachable from airframe  12 . In the illustrated embodiment, pylons  16   a ,  16   b  include receiving assemblies for coupling with pod assembly  50 . The connection between pylons  16   a ,  16   b  and pod assembly  50  may be a fixed connection that secures pod assembly  50  in a single location relative to airframe  12 . Alternatively, pod assembly  50  may be allowed to rotate and/or translate relative to airframe  12  during ground and/or flight operations. Airframe  12  preferably has remote release capabilities of pod assembly  50 . For example, this feature allows airframe  12  to drop pod assembly  50  at a desire location following transportation. In addition, this feature allows airframe  12  to jettison pod assembly  50  during flight, for example, in the event of an emergency such as a propulsion assembly or other system of aircraft  10  becoming compromised. One or more communication channels may be established between pod assembly  50  and airframe  12  when pod assembly  50  is attached therewith. A quick disconnect harness may be coupled between pod assembly  50  and airframe  12  such that flight control system  32  may send commands to pod assembly  50  to perform functions. For example, flight control system  32  may operate doors of pod assembly  50  between open and closed positions to enable loading and unloading of a payload to be transported within pod assembly  50 . 
     Aircraft  10  can implement the teachings of U.S. Pat. No. 10,618,646 B2, titled “Rotor Assembly Having a Ball Joint for Thrust Vectoring Capabilities;” and U.S. patent application Ser. No. 16/790,676, titled “Aircraft Having Redundant Directional Control,” the contents of which are hereby incorporated by reference. 
     Wings  14   a ,  14   b  and pylons  16   a ,  16   b  preferably include central passageways operable to contain flight control systems, energy sources, communication lines and other desired systems. For example, as best seen in  FIG. 1A , wing  14   a  houses the flight control system  32  of aircraft  10 . Flight control system  32  is preferably a redundant digital flight control system. In the illustrated embodiment, flight control system  32  is a triply redundant digital flight control system including three independent flight control computers. Flight control system  32  preferably includes non-transitory computer readable storage media including a set of computer instructions executable by one or more processors for controlling the operation of aircraft  10 . Flight control system  32  may be implemented on one or more general-purpose computers, special purpose computers or other machines with memory and processing capability. For example, flight control system  32  may include one or more memory storage modules including, but is not limited to, internal storage memory such as random access memory, non-volatile memory such as read only memory, removable memory such as magnetic storage memory, optical storage, solid-state storage memory or other suitable memory storage entity. Flight control system  32  may be a microprocessor-based system operable to execute program code in the form of machine-executable instructions. In addition, flight control system  32  may be selectively connectable to other computer systems via a proprietary encrypted network, a public encrypted network, the Internet or other suitable communication network that may include both wired and wireless connections. 
     Flight control system  32  communicates via communications network  24  with the electronics nodes of each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d , such as electronics node  32   a  of propulsion assembly  26   a  and electronics node  32   b  of propulsion assembly  26   b . Flight control system  32  receives sensor data from and sends flight command information to the electronics nodes of each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d  such that each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d  may be individually and independently controlled and operated. For example, flight control system  32  is operable to individually and independently control the operating speed and thrust vector of each propulsion assembly  26   a ,  26   b ,  26   c ,  26   d . Flight control system  32  may autonomously control some or all aspects of flight operation for aircraft  10 . Flight control system  32  is also operable to communicate with remote systems, such as a ground station via a wireless communications protocol. The remote system may be operable to receive flight data from and provide commands to flight control system  32  to enable remote flight control over some or all aspects of flight operation for aircraft  10 . The autonomous and/or remote operation of aircraft  10  enables aircraft  10  to perform unmanned logistic operations for both military and commercial applications. 
     Flight control system  32  can be coupled to rotor sensors  33   a  and  33   b , shown in  FIG. 1A  (rotor sensors  33   c  and  33   d  also present but not shown). Rotor sensors  33   a/b  can detect, measure, or otherwise monitor behavior of their respective rotor assemblies  34   a/b . Flight control system  32  can further be coupled to accelerometer  56 , vibration sensor  57 , and other sensors  58 , and  59 . A plurality of sensors are possible. Sensors may be used for temperature (at any point in the aircraft, such as near a rotor assembly, battery, or near a processor), propulsion system pitch angle, aircraft pitch, roll, and yaw axis orientation, acceleration, velocity, location such as by GPS (global positioning system), weight, altitude, elapsed service time, or to measure other onboard and external characteristics. Any of these sensors may be located anywhere that is useful on the aircraft  10 . For example, vibration sensors may be located on each rotor assembly  34 , as well as near a processor or near pod  50 . Temperature or pitch may be measured at different locations on the aircraft  10 . Communications interface  61  can be coupled to flight control system  32  and may provide wireless communication, such as with a flight control tower or system. Cellular, satellite, hardline, and other types of communication interfaces are possible. 
     Flight control system  32  may comprise a vehicle state estimator  200 , shown in a possible embodiment in  FIG. 2 . Vehicle state estimator  200  can receive vehicle or component information from database  210  and combine it with data from sensors  15  (such as sensors  56 ,  57 ,  58 ,  59  in  FIG. 1A ) to create a vehicle state health assessment  255 . The health state assessment can comprise a probability of failure related to the one or more hazards and calculated with the statistical failure data and the sensor data. The health state assessment can alternatively comprise a probability of success similarly calculated. Vehicle health state assessment may comprise an overall failure rate (e.g. 1×10 −7 ), that is a combined failure rate of various components (e.g. rotor assemblies with failure rates of 1×10 −8 , blades with rates of 1×10 −11 , clamps with rate of 1×10 −10 , combined to yield the overall failure rate). State estimator  200  can also receive a flight plan critical element data set, including hazards, from a GCS (ground control system)  220 , combine it with an onboard flight plan  240  from a vehicle monitoring system (preferably comprising flight control system  32 ) and an onboard physics based model  230  for modeling flight trajectory and behavior. Using the information from elements  220 ,  230 , and  240 , a failure trajectory calculator  250  portion of the vehicle state estimator  200  can compute flight trajectories and other outcome data due to a possible failure at any point in time or location relative to the flight plan and critical elements. For example, at a time t, failure may look like a controlled or uncontrolled decent to ground with a possible radius for a touchdown location. At probability calculation  260 , the state estimator can combine the vehicle state health assessment  255  with the failure trajectory calculator  250  to yield a probability of failure at a given location or plan segment, and/or at a given time or time period, t. The probability of failure, for example 1×10 −9 , can be compared to an approved probability  270 . Approved probability  270  can be express as a probability of success (e.g. 99.999%) or as a probability of failure (e.g. 1×10 −9 ), as long as the system and any users realize the method being used. At  280 , it is determined if the current mission is safe to continue. For example, if an approved probability of success is a failure rate of 1×10 −5  or better, then a failure rate of 1×10 −9  would mean it is safe to continue. When safe, the state estimator  200  would continue to create aircraft commands  295  so as to continue the flight plan route. Alternatively, if an approved probability were 1×10 −11 , then a failure rate of 1×10 −9  would fail, and state estimator  200  would activate a contingency maneuver or action at  285 . The contingency mission  290  would be carried out by the aircraft  10  before returning to the GCS flight plan  220  or VMS flight plan  240  for further instructions. The foregoing provides a brief description of the vehicle state estimator  200 , but more description can show the interplay among the various components. 
     Database  210  can comprise prognostic algorithms about vehicle maintenance, vehicle useful life data, or any type of statistical measure of vehicle or component useful life. This can include data regarding the predicted life of a rotor assembly  34 , or any other component of aircraft  10 . Rotors, blades, batteries, actuators, sensors, struts, clamps, welding, wings, engines, seals, adhesives, bolts, processors, communication interfaces, and other components may have estimated life cycles that can be used by the state estimator  200 . Database  210  can also provide state estimator with prognostic information related to diagnosing onboard problems. For example, when combined with data from sensors  15 , data from database  10  may assist flight control system  32  in realizing that a change in a pitch angle of the aircraft  10  may be due to a partial or whole loss of power in a rotor assembly  34 . This may change the estimated failure rate of the given rotor assembly  34 . For example, one rotor assembly may have an updated failure rate of 1×10 −3 , while the other rotor assemblies may stay at 1×10 −9 . The state estimator  200  of flight control system  32  may then adjust the capabilities of said rotor assembly for creating a vehicle state health assessment  255 . Temperature changes detected by sensors  15  may impact failure rates. For example, batteries or rotor assemblies may have decreased life expectancy at significantly higher or lower component or environmental temps. Altitude or pressure may change failure rates of processors, or accelerometers. At a given altitude, temperature, and pressure, a vehicle state health assessment may yield an expected failure rate of 1×10 −12 . An hour later, with minimal physical wear and tear on components, but the aircraft operating at a different altitude, temperature and pressure, an expected failure rate may be 1×10 −5 . The change in failure rate may be due to a chosen processor type that is susceptible to temperature changes, or possibly rotor assemblies that are susceptible to pressure and altitude extremes. Failure rates may also change over time. A rotor blade may have a given failure rate in its first year or first 100 hours of service, a different one in the second year or second 100 hours of service, and so forth. The vehicle health state assessment  255  can take into account failure rates for any component on the aircraft  10 . The monitoring of the vehicle health state assessment is preferably done in real time. Alternatively, time increments can be used as desired. 
     Database  210 , the data it tracks and stores, its communication with aircraft  10 , and how such data is used by aircraft  10  can make use of the teachings of the following references, which are hereby incorporated by reference: U.S. Pat. No. 9,096,327 B2, titled “Aircraft Health Assessment System;” U.S. Pat. No. 10,474,973 B2, titled “Aircraft Fleet Maintenance System;” U.S. Pat. No. 10,783,671 B1, titled “Systems and Methods for Aligning Augmented Reality Display With Real-Time Location Sensors;” U.S. patent application Ser. No. 16/186,158, titled “System and Method for Maintaining and Configuring Rotorcraft;” and U.S. patent application Ser. No. 15/879,207, titled “On-Component Tracking of Maintenance, Usage, and Remaining Useful Life.” 
     GCS flight plan  220  can provide state estimator  200  and aircraft  10  a flight plan  240  that is stored by the vehicle monitoring system (which preferably comprises part of flight control system  32 ). GCS flight plan  220  preferably will be sent by a GCS. Flight plan  240  preferably comprises a path of flight with trajectories and takeoff and landing information. Flight plans  220 / 240  preferably also comprises identified critical elements, such as hazards or areas with enhanced safety requirements. For instance, an identified area around a city or other congested population area may be a critical element, due to heightened safety requirements when compared to rural or other sparsely populated areas. Other possible critical elements can be ground features such busy roadways, high population density areas, schools, and critical infrastructure. Critical elements can be airspace related features such as congested and controlled airspaces or airspace boundaries. 
     Aircraft  10  can comprise communication interfaces to communicate with database  210  and GCS  205 . Cellular, satellite, hardline, Wi-Fi, Bluetooth, or other interfaces can be used. 
     The failure trajectory calculator  250  can use the flight plan  240  with identified critical elements, and a physics based model  230 , to identify decision points along the flight plan where the vehicle state estimator  200  would have to decide to proceed or activate a contingency based on the then-current vehicle state health assessment  255 . For example, an identified critical element may be a school. Entering airspace over a school, or radius around a school, can be identified as a decision point. At the decision point, the failure trajectory calculator  250  can be combined with the vehicle state health assessment  255  to calculate a probability of failure during an exposure time, such as flight time over the school. Such a probability can be compared to an accepted failure rate to determine whether to continue the original flight plan or to take a contingency action. An accepted failure and success rates may include rates satisfying those required and/or approved by the FAA (Federal Aviation Authority) or another regulatory, standards, or commercial entity. 
     One example of this system and method being used in practice can be seen in  FIG. 3 . Aircraft  300 , such as aircraft  10  with flight control system  32  described above, is on a mission with general path of flight  340 . At decision point  350  aircraft  300  will cross into airspace  320  above a school  310 . Airspace  320  may extend up to a certain altitude or extend infinitely high (or otherwise apply to flight at all altitudes). Airspace  320  may reside over the school and an additional radius r. Up to decision point  350 , aircraft  300  may be flying over a rural area with an approved probability of failure 1×10 −5 . At decision point  350 , and within airspace  320 , the approved probability of failure may change to 1×10 −10  due to the increased risk associated with a school. A vehicle state estimator in aircraft  300  may assess, via sensors and component life cycle data, that its current failure rate is 1×10 −8 . At element  280  of  FIG. 2 , such a failure rate would trigger a contingency  285 . There may be multiple contingencies available for aircraft, such as flight path  340   a  that goes around airspace  320 , flight path  340   b  that goes over airspace  320 , and flight path  340   c  that causes the aircraft to return to its base. Any desirable contingency is possible, including a contingency landing away from the school. Another contingency could be dropping a payload and then proceeding along the original flight path  340 . Another contingency plan could be flying in a holding pattern and/or awaiting an updated flight plan from a GCS or other source. 
     Elements  210 ,  212 ,  214 , and  15  generally give the state estimator  200  information about the status of the aircraft  10 . Elements  220 ,  240 ,  230  generally give the state estimator  200  information about a flight plan or external factors, such as physics-based models providing aircraft flight dynamics information, including due to gravity or other elements outside of the aircraft. 
       FIG. 4  displays a possible method embodiment under the present disclosure. Method  400  illustrates one example for how failure rates and sensor data can be combined and analyzed with failure probability rates to operate an unmanned aircraft. Method  400  comprises a method for operating an unmanned aircraft. At step  410 , a rotor failure rate of one or more rotors is received, by a VMS or the flight control system, or another control system or processor on the aircraft. At  420 , a battery failure rate is received for one or more batteries on the aircraft. At  430 , real-time operational data is received from one or more sensors on the aircraft. At  440 , a flight plan is received that has a route of travel and identified critical elements along the route. At  450 , the flight plan is begun. At  460 , upon nearing a critical element, the battery failure rate and the rotor failure rate are adjusted based on the real-time operational data to provide an adjusted battery rate and an adjusted rotor rate. At  470 , the adjusted battery rate and the adjusted rotor rate are multiplied to give a critical element failure rate related to the critical element. At  480 , the critical element failure rate is compared to an approved failure rate. If the critical element failure rate is greater than the approved failure rate, then at  485  a contingency action is activated. If the critical element failure rate is less than the approved failure rate, then at  490  the flight plan is continued. 
     Another example of a method embodiment can be seen in  FIG. 5 . Method  500  comprises a method of operating an unmanned vehicle. At  510 , a travel plan can be received, by a processor or control system, that identifies one or more critical elements (such as hazards, cities, schools, etc.) along a travel route. At  520 , statistical failure data related to one or more components of the unmanned vehicle can be received. At  530 , sensor data is collected from one or more sensors, the sensor data related to operational variables affecting the unmanned vehicle, including for example, onboard performance and condition information and external information, e.g. environmental conditions. At  540 , a health state assessment can be calculated that comprises a probability of success or failure related to the one or more critical elements, calculated using the statistical failure data and the sensor data. At  550 , the probability is compared to a selected probability. At  560 , if the probability of success or failure fails to satisfy the selected probability, a contingency plan or maneuver is activated. At  570 , if the probability of success or failure satisfies the selected probability, then the travel plan is continued. The comparison to an approved probability can take several forms. A minimum success rate, or maximum failure rate, can be set by the FAA. The comparison may comprise a “greater than,” “greater than or equal to,” “less than,” “less than or equal to” operation. The comparison can be with an approved, specified, or selection failure or success rate, set by the FAA or another group. 
     The flight control system  32  of  FIG. 1A , VMS, and or remote database  210  of  FIG. 2 , can store or access failure rates and other statistical data about life cycles or other capabilities of any appropriate component of an unmanned vehicle. For unmanned aircraft, such data may be related to structural strength, battery life, rotors, powerplant, actuators, sensors, seals, bolts or other joints, material strength such as in aluminum, plastic or steel components, and more. For unmanned land vehicles, data may relate to tire and brake wear, engine status, oil levels, and more. Data can be failure probability rates and/or prognosis data or predictive models for diagnosing failures in a component. 
     Sensors, such as sensors  56 ,  57 ,  58 ,  59  in  FIG. 1A , preferably relate to detecting characteristics that impact the components described above, though a variety of sensors can be used. Sensors can be used to detect any factor that may impact component life, such as temperature, pressure, vibration, torque, shear, compression, weight, CG, speed, location, altitude, presence of corrosive materials, direction of travel, electrical current for inputs and outputs, propulsion system output, energy source output or capacity, and more. Sensors for these factors can include thermometers, pressure sensors, pitch angle sensors, accelerometers, GPS units, force sensors, corrosive material detection sensors, and more. 
     A possible unmanned vehicle is shown in  FIGS. 1A-1F . However, a variety of unmanned vehicles are possible under the present disclosure. Unmanned vehicles can include air, land, and water vehicles, including for example drones, helicopters, airplanes, cars, tanks, trucks, boats, and more. 
     An algorithm for calculating an overall failure probability rate for an unmanned vehicle can vary depending on the exact component makeup of the vehicle. An unmanned aircraft  10 , shown in  FIG. 1A-1F , may have four rotor assemblies and four batteries. Such an aircraft also has a given material composition, some of its parts are metals such as aluminum alloys, other components are plastics, composites, or compounds. The exact algorithm for calculating an overall (vehicle-wide) failure rate would be different than another vehicle. 
     An unmanned aircraft with a more standard airplane form, such as fixed wing aircraft  600  in  FIG. 6 , may have a turbofan engine  640 , wings  620 , fuselage  610 , and stabilizers  630 , that aircraft  10  does not have. Aircraft  600  may comprise other components, such as battery  660 , component A  670 , and component B  680 . Sensors  690  may be disposed at various locations on aircraft  600 . Sensors  690  may measure operating characteristics of turbofan engine  650 , wings  620 , fuselage  610 , fins  640 , battery  660 , component A  670 , or component B  680 . Some of sensors  690  may additionally, or alternatively, measure other factors, such as ambient temperature, pressure, speed or other characteristics such as described elsewhere in the present disclosure. Considering aircraft  600 , an equation for calculating an aircraft probability of failure, such as during flight or when encountering a hazard or critical element, may look like the following: 
       Prob. of failure=(FR engine )(FR wing )(FR batt. )(FR fin )(FR A )(FR B ) 
     In this equation FR is the failure rate for a given component. Any failure rate can be rewritten as a success rate, as long as such formatting is held consistent in the given equation. Failure rates, or success rates, can be given as a percentage (%) or as a value, such as 0.99, or 0.001. 
     In contrast, the equation for aircraft  10  of  FIGS. 1A-1F  might be as follows: 
       Prob. of failure=(FR wing )(FR batt. )(FR stab )(FR rotor )(FR elec motor )(FR A )(FR B ) 
     Aircraft  10  has no turbofan engine, so that failure rate is left out. Furthermore, the wings  12 , battery  61 , and fins  20  have different form factors and other characteristics, so their failure rates will be different than those of aircraft  600 . Instead of a turbofan engine, aircraft  10  has an electric motor, and rotors. Failure rates for these components are included in the equation for aircraft  10 . Components A and B can refer to other components of aircraft  10  that may not be part of aircraft  600 . 
     It is to be understood than any failure rate, such as FR engine , may itself be calculated from various components that comprise an engine, for example. The failure rate of an engine may be calculated by multiplying together the failures rates for seals in the engine, a failure rate of a fuel injector, or compression chamber, or other components. 
     Sensors  690  in  FIG. 6 , or sensors  56 ,  57 ,  58 ,  59  in  FIG. 1A , may impact a failure rate for a component. Statistical data and prognosis data, such as from database  210  in  FIG. 2 , may also impact a failure rate. For example, a given battery model, from a given manufacturer, may have a given failure rate. When an aircraft is approaching a hazard or critical element, possibly airspace around a city, a flight control system and vehicle state estimator may use the failure rate of the battery when calculating a state health assessment with a failure or success rate. When approaching airspace over a city, which may have heightened safety requirements, the flight control system may analyze sensor data, such as battery operating temperature or battery output, and combine this data with statistical data related to the battery model. Such an analysis may reveal that the battery is not operating at full strength, and may have been in use for two years, and the failure rate may need to be replaced or adjusted. For example, FR batt  may need to be adjusted from 0.0000001 to 0.000002. This analysis can occur with a plurality of components, using sensor data and statistical and predictive data, during a flight. These analyses can be ongoing and performed in real-time, or at given intervals, such as when approaching a hazard or at a decision point. In some cases, an analysis may show that the aircraft has an overall failure rate that is too high to fly over a city. The same aircraft, with the same overall failure rate, may be allowed to fly across a different hazard, such as a highway that is transited with a briefer exposure time and unlikely to have individual exposed outside of vehicles or buildings, or an unpopulated area. 
     A possible method for using sensor data or statistical data to replace or adjust the failure rate of a component can be seen in  FIG. 7 . In method  700 , at  710 , the failure rate for a component is accessed or received. At  720 , sensor data or statistical data that may impact the component failure rate is accessed or received. At  730 , it is determined whether the sensor or statistical data mandates an adjusted or different failure rate for the component. If no, then at  740 , the original failure rate is used, such as in the equations described above. If yes, then an adjusted or different failure rate is used. A different failure rate may be mandated by the age of the component or temperature, or other data. Or sensor or statistical data may say to apply a factor, such as 0.99, to the original failure rate to reach an adjusted failure rate. 
     Although the present invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.