An Efficient Path Planning Approach for Autonomous Multi-Uav System in Target Coverage Problems

Abstract

Purpose - The purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems. Design/methodology/approach - An enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy. Findings - Numerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively. Practical implications - Simulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems. Originality/value - The proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.