An efficient path planning approach for autonomous multi-UAV system in target coverage problems

Abstract

PurposeThe purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems.Design/methodology/approachAn 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.FindingsNumerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively.Practical implicationsSimulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems.Originality/valueThe 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.