## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.3 Target Search and Surveillance  
Target search path planning deals with searching for targets with no or minimal information on the target of concern. Surveillance deals with repeated coverage and search of a specified area to obtain the desired information. Search problems are generally defined by generating a grid of cells over an environment. Poor information about target locations and noisy sensors can increase the difficulty of quickly and easily finding targets. One regional surveillance method to ensure maximum coverage is the _lawnmower_ path definition, sometimes referred to as a _bostrophedon_ pattern. The pattern is efficient for ensuring maximum coverage of an area. However, it is very time consuming, and depending on the requirement of the mission, may be ineffective for the needs of the operator. Similarly, a spiral pattern that slowly spirals in either smaller or larger radius could provide similar results. One challenge is determining how long or how many times a vehicle must survey a point before a satisfactory level of confidence that a target exists in a given area. [PERSON] and [PERSON] [76] proposed a robust UAS search method for determining target existence with the consideration that the prior probabilities for a given cell are poorly known. The use of Beta distribution enabled a prediction of the number of searches required in a given cell to achieve the desired confidence that a target exists in a given area. [PERSON] _et al._[61] proposed a pheromone-based algorithm with an artificial potential field to perform regional surveillance with multiple UASs. A region would be separated into multiple units and a pheromone model would be applied to each unit. Pheromones have a diffusion feature that results in a portion of its information being translated to the units around it, using either an attractive or repulsive factor. This information then results in a gradient of pheromones being formed providing a path for the UAS to follow. An artificial potential field was then used to aid in obstacle avoidance, collision avoidance, and optimal search. A planning algorithm by [PERSON] _et al._[77] for optimal monitoring of spatial environmental phenomena based on Gaussian process priors showed improvement at finding global maximum conditions. This algorithm would be valuable for surveying unknown spatio-temporal fields such as gas plumes and humidity. [PERSON] and [PERSON] [78] propose a search algorithm for multiple-vehicle maritime search and rescue that accounts for target drift using a mixed integer linear program, relying on a model over multiple periods to account for object location over time. [PERSON] and [PERSON] [79] developed a method to track the boundary of an oil spill using model predictive control and universal kriging. Universal kriging is an interpolation technique closely related to regression analysis. By combining universal kriging and model predictive control they proposed a method to search the environment with a sensor and, based upon the initial samplings, develop a means to track the boundary of the oil spill. [PERSON] _et al._[80] provided a multi-agent information fusion and control scheme for target searching. An individual probability map for target location(s) was maintained by each vehicle and updated, based on measurements made by the vehicle, using Bayes' rule. A consensus-like distribution fusion scheme, updated with asynchronous information, was used to create a multi-agent probability map for target existence. A distributed multi-agent coverage control method for path planning, using a Voroni partition, that ensured a sufficient number of visits to each cell was performed. [PERSON] and [PERSON] [81; 82] proposed a method of decentralized cooperative control of multiple-UASs performing multiple tasks in an urban environment. The construct assumed limited communication between the vehicles and considered potential line of sight impacts from buildings.