Multi UAV collaborative search path optimization with improved particle swarm optimization algorithm

DOI:	10.11809/bqzbgcxb2025.01.028
Keywords:	multi UAV; particle swarm optimization; ant colony optimization; cooperative search; path optimization
Abstract:	The Particle Swarm Optimization (PSO) algorithm is widely employed in the field of target search due to its advantages of low computational complexity, simple algorithmic structure, and rapid convergence. However, when multiple unmanned aerial vehicles (UAVs) collaborate using the traditional PSO for target search, a significant redundancy in the search paths emerges. This phenomenon is attributed to the inherent stochastic nature of the PSO. Furthermore, the unfiltered sharing of information within the swarm leads to historical information being incorporated into the algorithmic computations during the iterative process. Consequently, the algorithm inadvertently iterates over previously traversed waypoints, resulting in duplicated search paths and additional resource consumption. To address this issue, this paper proposes an improved particle swarm algorithm for multi UAV search. The algorithm applies the conventional PSO to the collaborative target search problem involving multiple UAVs. Subsequently, the ACO is employed to filter the shared location information within the swarm, aiming to compute refined position data. This filtered positional information is then utilized in the iterative process of the PSO algorithm to calculate the expected velocity of the UAVs. The UAVs, guided by the computed expected velocities, effectively mitigate the issue of redundant search paths. Simulation results presented in the paper substantiate the efficacy of this search algorithm in significantly reducing the repetition rate of search paths and, consequently, diminishing the overall search distance covered by the UAVs.
Issue:	Vol. 46 No. 1 (2025)
Published:	2025-01-31
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