Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration  

作　　者：Qiming Liu Xinru Cui Zhe Liu Hesheng Wang 

机构地区：[1]Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China [2]MoE Key Laboratory of Artificial Intelligence,AI Institute,Shanghai Jiao Tong University,Shanghai 200240,China [3]Department of Automation,Key Laboratory of System Control and Information Processing of Ministry of Education,Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education,Shanghai Engineering Research Center of Intelligent Control and Management,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第9期1933-1943,共11页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China (62225309,62073222,U21A20480,62361166632)。

摘　　要：Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关 键 词：Graph neural networks(GNNs) spatial memory topological map visual navigation 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]