Title: Pheromone-inspired multiple moving targets search method for swarm unmanned aerial vehicles in environments with unknown obstacles

Authors: Mao Wang; Shaowu Zhou; Hongqiang Zhang; Lianghong Wu

Addresses: School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, China ' School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China ' School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China ' School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, China

Abstract: A multiple moving targets search problem for swarm UAVs in environments with unknown obstacles is studied. The search is divided into roaming search and collaborative search; the multitarget search algorithm consists of task allocation, roaming search, collaborative search and obstacle avoidance. To convert between collaborative search and roaming search, a distance-based dynamic task allocation strategy is proposed. A confidence area pheromone for roaming search is proposed to reduce the repeated search times conducted in the same areas. Probabilistic finite PSO is proposed to adapt to search for moving targets in collaborative search. Furthermore, a boundary scanning-based obstacle avoidance strategy is improved to achieve efficient obstacle avoidance for UAVs in a grid environment. Based on the above, a multiple moving-target search algorithm mode is constructed. This mode shows better performance than existing methods as verified through simulation experiments, and provides a helpful alternative in postdisaster search, and other search fields.

Keywords: swarm unmanned aerial vehicles; multiple moving targets search; confidence area pheromone; probabilistic finite particle swarm optimisation; PFPSO.

DOI: 10.1504/IJBIC.2025.146391

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.3, pp.139 - 155

Received: 06 Nov 2023
Accepted: 17 Apr 2024
Published online: 28 May 2025 *

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