PSO algorithm for UAV autonomous path planning with threat and energy cost optimization

Abstract

The PSO (Particle Swarm Optimization) algorithm uses a population of single particles, randomly distributed over the function search space, in order to construct an optimal flight path. During the algorithm iteration execution, each particle evaluates its fitness function according to the actual position. Then, every particle moves towards a direction dependent on its actual best position and the position of the best particle so far. The procedure ends when the iteration limit is reached or fitness criterium is met. In the presented autonomous UAV 3D path planning method, a PSO algorithm is used to build a feasible path, optimized in terms of fuel and threat cost. The process begins when the UAV detects or receives information about threats appearing on its primary trajectory. The population of particles moves in the search space of three variables, which are the angles of pitch, roll and yaw. These angles determine the spatial orientation of the UAV, indicating its direction of movement in each step. Further waypoints are chosen with consideration of the distance to the target and violation of threat areas. As a final result, the PSO algorithm constructs a suboptimal, feasible path which could be used as a reference trajectory for the UAV’s automatic control system. Simulation results turned out to be completely repeatable and indistinguishable, despite the stochastic nature of the algorithm, which proves its great optimization abilities. Moreover, its short execution time (within seconds) allows this procedure to be used in real time applications.