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An improved gravitational search algorithm and its performance analysis for multi-robot path planning

机译:改进的重力搜索算法及其在多机器人路径规划中的性能分析

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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 clutter Environment. IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots on the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSAs. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental result of the multi-robot path planning were compared with those obtained by IGSA, ABC (Artificial Bee Colony) and DE (Differential Evolution) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to ABC and DE with respect to the average total trajectory path deviation and average uncovered trajectory target distance.
机译:本文提出了一种在杂乱环境下使用改进的引力搜索算法(IGSA)来优化多机器人路径轨迹的新方法。 IGSA技术是在动态框架中并入多机器人系统中的,它将提供强大的性能,确定性的协作以及应对恶劣环境的能力。团队中的机器人可以使用已开发的IGSA进行独立决策,相互协调和合作以实现共同的目标。已经使用IGSA开发了路径规划方案,以从拟议环境中的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,ABC(人工蜂群)和DE(差分进化)在类似环境中获得的结果进行了比较。在平均总轨迹路径偏差和平均未发现轨迹目标距离方面,仿真和Khepera环境结果显示出IGSA优于ABC和DE。

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