Reinforcement Learning for Robot Navigation Using Hex-grid Maps

• 作 者: Zhu Yuanyang Xie Donghan Wei Qing Chen Chunlin
• 机 构: Department of Control and Systems Engineering, School of Management and Engineering,Nanjing Universi
• 出 处: 第20届中国系统仿真技术及其应用学术年会(20th CCSSTA 2019)
• 发表日期: 2019年7期
• 关键词: Reinforcement Learning Hex-gird Map Robot Navigation Q-learning

　　For some rodent mammals when they foraging or looking for a target, the positions and headings in their brain cells are distributed as a hexagonal raster shape.Hex-grid maps used in the navigation of the enhanced learning robots may have a better performance than the traditional squaregrid maps.In this paper, a new reinforcement learning based robot navigation method is proposed using hex-grid maps.The hex-grid map-building methods and the reinforcement learning based navigation approach in a hex-grid environment are introduced consequently.Several groups of comparative simulation experiments are carried out and the results demonstrate the advantages and efficiency of the proposed method.