An improved neuro-dynamics-based approach to online path planning for multi-robots in unknown dynamic environments

Xin Yi, Anmin Zhu
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引用次数: 4

Abstract

Online path planning for multi-robots in complicated and dynamic environments is a difficult and hot issue in the field of robotics. Many traditional path planning methods cannot meet the requirements of online and real-time processing. Neuro-dynamics-based method has an aptitude for online and real-time path planning in complicated and dynamic environments. However, this method still has shortcomings. In this paper, an improved neuro-dynamics-based method is proposed with advantages. It has conducted efficient performance and easier realization with much less computational time complexity. Meanwhile, by entering “repulsion” mechanism, the improved method is capable of fair allocation and load balancing on the limited resources. Both simulated experiments and theoretical analysis demonstrate the feasibility and availability of the improved method in the paper.
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未知动态环境下多机器人在线路径规划的改进神经动力学方法
复杂动态环境下多机器人的在线路径规划是机器人领域的一个难点和热点问题。许多传统的路径规划方法不能满足在线和实时处理的要求。基于神经动力学的方法在复杂动态环境下具有在线实时路径规划的优势。然而,这种方法仍然有缺点。本文提出了一种改进的基于神经动力学的方法。该算法性能高效,易于实现,计算时间复杂度低。同时,通过引入“斥力”机制,改进后的方法能够在有限的资源上实现公平分配和负载均衡。仿真实验和理论分析都证明了改进方法的可行性和有效性。
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