基于改进生物激励神经网络算法的多移动机器人协同变电站巡检作业
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作者单位:

湖南大学 电气与信息工程学院,长沙 410082

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E-mail: chennk@hnu.edu.cn.

中图分类号:

TP273

基金项目:

国家自然科学重点基金项目(61733004).


Multi-mobile robot cooperative inspection operation based on improved biological excitation neural network algorithm in substation
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College of Electrical and Information Engineering,Hunan University,Changsha 410082,China

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    摘要:

    针对大型变电站巡检作业效率低的问题,利用改进的生物激励神经网络算法和优先级启发式算法,结合基于变切线长的无障碍物区域分割法,提出一种多移动机器人协同全区域覆盖巡检以及多任务点协同巡检的方法.首先,分析生物激励神经网络算法的不足,如规划的路径曲折、转角大等问题,并提出一种改进方法,利用改进的算法和Hungarian算法实现对多任务点的巡检;然后,设计一种变切线法将电站区域分解成若干不含障碍物的子区域,各移动机器人分别在子区域内利用优先级启发式算法选择路径,利用改进的生物激励神经网络算法跳出死区,从而完成多机器人的协同全区域巡检任务;最后,通过仿真实验表明,改进的神经网络算法相比于原始算法与A*算法在路径长度和转向次数等方面具有明显的优化作用,仿真实验验证了所提出多机器人协同巡检方案的可行性.

    Abstract:

    A multi-mobile robot cooperative method is proposed to overcome the problem of low inspection efficiency in the large substation, by using the improved biological excitation neural network algorithm in this paper. Firstly, the shortcoming of the biological excitation neural network algorithm is analyzed, such as tortuous paths, large turning angle, and then an improved method is proposed. Combining the improved method and Hungarian algorithm, the cooperative inspection task of multi-robot for multi-task points is completed. Then, a variable tangent method is designed to decompose the substation area into several sub-regions without obstacles, and the priority heuristic algorithm is proposed for the robots to complete the full-area inspection task, meanwhile the improved method also be used for the robots to jump out the dead zone. Simulation experiments show that compared with the original algorithm and A* algorithm, the improved algorithm has obvious optimization effects in terms of path length and turning times, and the feasibility of the multi-robot collaborative inspection scheme is also proved throught the simulation.

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引用本文

陈南凯,王耀南,贾林.基于改进生物激励神经网络算法的多移动机器人协同变电站巡检作业[J].控制与决策,2022,37(6):1453-1459

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  • 在线发布日期: 2022-04-22
  • 出版日期: 2022-06-20