Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network

• Main Authors: Chaoliang Zhong, Shirong Liu, Qiang Lu, Botao Zhang
• Format: Article
• Language: English
• Published: SAGE Publishing 2017-11-01
• Series: International Journal of Advanced Robotic Systems
• Online Access: https://doi.org/10.1177/1729881417743612

This article proposes an experience-based route map continuous learning method and applies it into robot planning and navigation. First of all, the framework for robot route map learning and navigation is designed, which incorporates the four cyclic processes of planning, motion, perception, and extraction, enabling robot to constantly learn the information of the road experience and to obtain and improve the route map of the environment. Besides, a growing-on-demand self-organizing neural network learning algorithm is also proposed. This algorithm is based on growing neural gas algorithm, but it does not require presetting of network scale, and under the condition of dynamically growing input data, it can regulate the increase scale of network online in a self-adaptive and self-organized manner to obtain stable learning results. Finally, with robot roaming in an environment, this algorithm is used to conduct continuous learning of dynamically increasing route information, extract the topological structure of the raw road data in feature space, and ultimately obtain the route map of the environment. Mobile robot utilizes the route map to plan a suitable route and guides robot to move to the destination along the route and complete navigation task. Through physical experiments in outdoor environment, its feasibility and validity are verified.