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Multi-robot path planning in a dynamic environment using improved gravitational search algorithm

机译:使用改进的重力搜索算法的动态环境中的多机器人路径规划

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This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA) in a dynamic environment. GSA is improved based on memory information, social, cognitive factor of PSO (particle swarm optimization) and then, population for next generation is decided by the greedy strategy. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position. Finally, the analytical and experimental results of the multi-robot path planning have been compared with those obtained by IGSA, GSA and PSO in a similar environment. The simulation and the Khepera environmental results outperform IGSA as compared to GSA and PSO with respect to performance matrix.
机译:本文提出了一种在动态环境中使用改进的重力搜索算法(IGSA)来优化多机器人路径轨迹的新方法。根据记忆信息,社交,PSO(粒子群优化)的认知因素对GSA进行改进,然后由贪婪策略决定下一代的种群。已经使用IGSA开发了路径规划方案,以从现有位置最佳地获取机器人的后续位置。最后,将多机器人路径规划的分析和实验结果与IGSA,GSA和PSO在类似环境中获得的结果进行了比较。在性能矩阵方面,与GSA和PSO相比,仿真和Khepera环境结果优于IGSA。

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