Adaptive Resonance Theory-Based Global Topological Map Building for an Autonomous Mobile Robot

3D space perception is one of the key technologies for autonomous mobile robots that perform tasks in unknown environments. Among these, building global topological maps for autonomous mobile robots is a challenging task. In this study, we propose a method for learning topological structures from unknown data distributions based on competitive learning, a type of unsupervised learning. For this purpose, adaptive resonance theory-based Topological Clustering (ATC), which can avoid catastrophic forgetting of previously measured point clouds, is applied as a learning method. Furthermore, by extending ATC with Different Topologies (ATC-DT) with multiple topological structures for extracting the traversable information of terrain environments, a path planning method is realized that can reach target points set in an unknown environment. Path planning experiments in unknown environments show that, compared to other methods, ATC-DT can build a global topology map with high accuracy and stability using only measured 3D point cloud and robot position information.