## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.1 Fixed Target  
Fixed target path planning deals with the algorithms utilized with visiting fixed locations for information gathering or support. UASs are currently being used in missions that require the vehicle to visit a set of targets and maintain an optimum flight path to complete their tasks. Many of the fixed target path planning problems can be considered similar to the traveling salesman problem (TSP) that has been evaluated significantly in numerous ways for decades since [PERSON] _et al._, developed a solution as an integer linear program [16]. The TSP type problem has been approached with multiple solutions. Many of the evolutionary algorithms used to solve these NP-hard (short for _non-deterministic polynomial-time hard_, widely taken to imply that the problem is computationally intractable [17]) problems provide acceptable results, although many of them have constrained the problems to some level. Early approaches in solving the path planning problem included the use of a Tabu Search (TS) heuristic algorithm [18; 19]. The TS can provide a solution that allows for progression without becoming trapped in local optima. [PERSON], _et al._[20] used a Reactive Tabu Search method to solve UAS routing in the construct of a multiple Traveling Salesman Problem with time windows. Their objective was to maximize expected target coverage while incorporating weather and a survival probability at each target as random inputs. [PERSON] _et al._[21] proposed a Tabu (Taboo in their paper) Search algorithm for multiple task planning for multiple UASs that showed better performance than genetic algorithms or ant colony optimizations. [PERSON] and [PERSON] [22] utilized a Tabu search algorithm to develop their path and time. Numerous methods have been developed more recently that provide better results to the path-planning problem than the Tabu Search, although it still is valuable for certain processes. Additionally, TS algorithms are only useful for small problems that do not consider vehicle availability or tasks that require constraints beyond simple time ordering. In 1956, [PERSON] proposed an algorithm for finding the shortest path between two points [23]. This algorithm is a key method for finding shortest paths for robots and unmanned vehicles and is used for numerous applications. The use of [PERSON]'s algorithm can be found in countless applications to UAS path-planning applications [24; 25; 26; 27; 28; 29]. [PERSON] _et al._[30] proposed a method of path planning that utilized Voronoi Diagrams and Discrete Particle Swarm Optimization (DPSO). A Voronoi diagram depicts lines that are equidistant to the closest neighboring points of interest, resulting in areas that define all points closest to the points of interest. A Voronoi diagram works similarly to and in conjunction with [PERSON]'s algorithm. The lines from the Voronoi were used as an initial path, with the points of interest being threats to be avoided. A DPSO algorithm was then used for simultaneous target attacks by multiple vehicles. Receding horizon control (RHC) [31] is a feedback control technique, also referred to as model predictive control, which is used across a large variety of applications. Receding horizon control is used as part of numerous algorithm types for UAS path planning. The advantage of RHCs is that it enables control of systems with a large number of inputs and outputs, especially for systems with complex objectives and strong nonlinear dynamics and constraints. The use of future considerations and predictions while optimizing the current time requirements is the key feature of RHC. Multiple path-planning algorithms utilize a form of RHC as part of their planning processes. The RHC control scheme has become useful for UAS algorithms due to the limited requirements for computational resources when compared to algorithms that perform global planning methods. [PERSON] _et al._, developed a decentralized RHC for multi-vehicle guidance [32; 33].