A Common-Neural-Pattern Based Reasoning for Mobile Robot Cognitive Mapping

Abstract

Neural Pattern-Based Reasoning for real-world robot navigation problems is proposed. Based on the concept of Pattern-Based Reasoning, the method enables a mobile robot to solve goal-oriented navigation problems in a remarkably short time with low memory consumption. Given a simple learning environment, the observed input vectors are processed by a Self-Organizing Incremental Neural Network (SOINN) to generate Spatial Common Patterns (CPs), which are useful in other unfamiliar environments. Performing goal-oriented navigation in unfamiliar environments, with prior information neither of the map nor the goal, the robot recognizes the partial area by referring to the nearest CPs and forming a pattern of CPs called A-Pattern. The sequential A-Patterns are used to derive the map of the environment. This map is optimized based on reasoning, as the new transitions between areas could be generated automatically. The method is evaluated by solving one real-world maze and three Webots simulated mazes. The results show that the proposed method enables the robot to find the markedly shorter path in only one episode, whereas use of the Reinforcement Learning requires more episodes. The map contains more information than the current hybrid map building or topological map. The map does not rely on coordinate, resulting in non-sensitivity to the error in self-pose estimation.