摘要
针对移动机器人环境认知问题,受老鼠海马体位置细胞在特定位置放电的启发,构建动态增减位置细胞认知地图模型(Dynamic growing and pruning place cells-based cognitive map model,DGP-PCCMM),使机器人在与环境交互的过程中自组织构建认知地图,进行环境认知.初始时刻,认知地图由初始点处激活的位置细胞构成;随着与环境的交互,逐渐得到不同位置点处激活的位置细胞,并建立其之间的连接关系,实现认知地图的动态增长;如果机器人在已访问区域发现新的障碍物,利用动态缩减机制对认知地图进行更新.此外,提出一种位置细胞序列规划算法,该算法以所构建的认知地图作为输入,进行位置细胞序列规划,实现机器人导航.为验证模型的正确性和有效性,对Tolman的经典老鼠绕道实验进行再现.实验结果表明,本文模型能使机器人在与环境交互的过程中动态构建并更新认知地图,能初步完成对Tolman老鼠绕道实验的再现.此外,进行了与四叉树栅格地图、动态窗口法的对比实验和与其他认知地图模型的讨论分析.结果表明了本文方法在所构建地图的简洁性、完整性和对动态障碍适应性方面的优势.
Aiming at environmental cognition problem of mobile robot,inspired by the activation of hippocampal place cells in particular regions,a dynamic growing and pruning place cells-based cognitive map model(DGPPCCMM)is established,which enables robot to construct the cognitive map self-organizingly by interacting with the environment and to implement environmental cognition.In the beginning,cognitive map consists of the activated place cell responding to current region;With the interaction with the environment,the responding activated place cells at different regions are gradually obtained,and the relationship among them is established,thus realizing the dynamic growing of the cognitive map;If new obstacles are discovered in the visited area,the cognitive map is updated using dynamic pruning mechanism.Besides,a sequence planning algorithm of place cells is proposed to realize robot navigation,which uses the constructed cognitive map as input.To verify the correctness and validity of the model,the classical Tolman detour task was reproduced.Results show that the model can enable robot to construct and update the cognitive map dynamically in the process of interacting with the environment,and to complete the reproduction of the Tolman detour task substantially.In addition,comparative experiments with occupancy grids,dynamic window approach and discussion about other cognitive map models are carried out,and results show the advantages of the proposed methods in the aspects of simplicity,completeness of the constructed cognitive maps and adaptability to dynamic obstacles.
作者
阮晓钢
柴洁
武悦
张晓平
黄静
RUAN Xiao-Gang;CHAI Jie;WU Yue;ZHANG Xiao-Ping;HUANG Jing(Faculty of Information Technology,Beijing University of Technology,Beijing 100124;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124;College of Electrical and Control Engineering,North China University of Technology,Beijing 100144)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2021年第3期666-677,共12页
Acta Automatica Sinica
基金
国家自然科学基金(61773027,61573029,61903006)
北京市自然科学基金(4204096)资助。
