Path Planning for Multi-UAV Target Coverage via Improved GA-PSO Algorithm
Chenyang Zhang, Zhiling Jiang, Hui Tang, Guanghua Song · 2024
The study of path planning problems has wide-spread applications in the automation and intelligence of unmanned aerial vehicles (UAVs). Metaheuristic algorithms have shown promising results in optimizing path planning tasks. Particle swarm optimization (PSO) is a frequently used metaheuristic algorithm, known for its fast convergence, strong search capabilities, and robustness. However, it also has the drawback of easily falling into local optima. We have implemented various optimizations to the PSO algorithm and proposed the comprehensively optimized grouping PSO (COGPSO) algorithm to better address path planning tasks. Multiple experiments were conducted to compare and analyze our algorithm with other PSO variants. Furthermore, we integrated Genetic Algorithm (GA) to tackle the multi-UAV target coverage problem.