Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

Abstract

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

Distributed Path Planning of Unmanned Aerial Vehicle Communication Chain Based on Dual Decomposition

“…It can also be used as bait to attract enemy firepower, or as a target aircraft for army training. In civil use, UAV can be used for aerial photography, disaster monitoring, traffic patrol, and security monitoring [3].…”