Genetic Fuzzy Single and Collaborative Tasking for UAV Operations
Nicholas D. Ernest, Anoop Sathyan, Kelly De Oliveira Cohen · ISTE eBooks · 2020
Abstract: Fuzzy logic is used in a variety of applications because of its attributor as a universal approximator attribute and nonlinear characteristics. However, it takes a lot of trial and error to come up with the best set of membership functions and rulebase that will effectively work for a specific application. This process can be simplified by using a heuristic search algorithm such as the genetic algorithm (GA). In this chapter, genetic fuzzy logic is applied to the task assignment of cooperating unmanned aerial vehicles (UAVs) classified as the polygon visiting multiple traveling salesman problem (PVMTSP). The PVMTSP has a lot of applications, including UAV swarm routing. This chapter discusses a method of genetic fuzzy clustering that would be specific to PVMTSPs and hence more efficient compared to k-means and c-means clustering. Two different algorithms based on genetic fuzzy logic are discussed: one evaluates the distance covered by each UAV to cluster the search space and the other uses a cost function that approximates the distance covered, thus resulting in a reduced computational time. The two approaches are compared to each other as well as to an already benchmarked fuzzy clustering algorithm. This chapter also discusses the scalability of our algorithm to increasing numbers of targets. The results are compared for small and large polygons.