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Localization of multiple odor sources using modified glowworm swarm optimization with collective robots

机译:使用改进的萤火虫群​​优化和集体机器人对多种气味源进行定位

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A multi-robot cooperation strategy based on a modified glowworm swarm optimization (M-GSO) is proposed. This strategy includes global random search of self-exploration, local search based on GSO algorithm and odor source declaration. And forbidden area setting is also introduced into the iteration process to achieve localization for multiple odor sources. This mechanism can ensure robots to start searching for the next odor source after the discovery of an odor source and ensure that other robots would not re-locate this odor source. Simulation results show that the proposed M-GSO can effectively enable the robot system to search and find all the odor sources existed in the indoor environment quickly and accurately.
机译:提出了一种基于改进的萤火虫群​​优化算法(M-GSO)的多机器人合作策略。该策略包括自我探索的全局随机搜索,基于GSO算法的局部搜索和气味源声明。禁止区域设置也被引入到迭代过程中,以实现多种气味源的定位。此机制可以确保发现臭味源后机器人开始搜索下一个臭味源,并确保其他机器人不会重新定位该臭味源。仿真结果表明,所提出的M-GSO可以有效地使机器人系统快速,准确地搜索和发现室内环境中存在的所有臭味源。

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