Aiming at the problems that traditional Artificial Fish Swarm Algorithm(AFSA)is easily falling into the local optimal solution and the planned optimal path is too long in the process of pasture inspection path planning,an improved Artificial Fish Swarm Algorithm integrated with Genetic Algorithm(GA)is proposed to obtain a Genetic Fish Swarm Algorithm(GFSA).On the basis of AFSA,GFSA accelerates the convergence speed and accuracy during the solving process by optimizing the vision and step length.Additionally,the algorithm avoids premature convergence around local optimal solution during iterations by designing a segmented crowding factor and incorporating mutation operations from Genetic Algorithm(GA).Genetic Fish Swarm Algorithm(GFSA)was implemented on a pasture inspection robot for experimental validation.The results showed that in ablation experiments,the path lengths planned by GFSA were consistently shorter than those of the comparison algorithms,with a median optimal path length of 23.2 m.In multi-algorithm comparison experiments,GFSA achieved the shortest paths and smaller turning angles compared to the other algorithms.In path planning on maps with different obstacle rates,GFSA's initial value and growth slope of the optimal path length were both lower than those of the comparison algorithms,demonstrating better robustness and adaptability.
关键词
牧场/巡检机器人/遗传算法/人工鱼群算法/路径规划
Key words
ranch/inspection robots/Genetic Algorithm(GA)/Artificial Fish Swarm Algorithm(AFSA)/path planning
维普
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