一种基于连接机制和时空经验的认知地图学习与导航方法

提出了一种连接主义方法, 利用移动机器人自身的时空经验, 在缺乏全局坐标信息和环境先验模型的情况下, 建立面向目标的认知地图. 在线形成的时序处理网络 (TSPN)可提供简洁的历史感知信息, 以神经元激活特性保存空间知识, 引导机器人运动. 结合TSPN和反应式行为模块的导航系统可实现动态的路标及方向检测、路径学习和实时导航功能. 仿真和实际实验验证了系统的有效性和适应性.

Connectionist approach for cognitive map learning and navigation based on spatio-temporal experiences

A connectionist method is proposed for mobile robot, which lacks a priori environmental model and global localization information, to learn goal directed cognitive map from its own spatio temporal experiences. Temporal sequence processing network (TSPN), which is constructed at run time, provides compact representations of history perceptive information, transforms spatial knowledge into cell firing characteristics and retrieves them in later runs to guide the robot. The navigation system integrating TSPN and a reactive safeguard module performs dynamic landmark and heading detection, route learning and collision free real time navigation in noisy environments. The simulation and real world experiments demonstrate the effectiveness and flexibility of the system.