Connectionist approach for cognitive map learning and navigation based on spatio_temporal experiences
LIU Juan
CAI Zi-xing
TU Chun-ming
Abstract：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.
Keywords：connectionist modelspatio_temporal reasoningmobile robotcognitive mapnavigation
Publication Date：2003-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 161-167 )