Abstract
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 structur...
Highlights
Human beings and animals exhibit superior capabilities in spatial cognition and navigation even in a complex largescale environment or without complete and accurate perception information
As an important kind of behavioral strategy in human navigation, route-based navigation strategy has been used in autonomous mobile robots, that is, a process of using real-time perceived environment information and selfmotion ability to reach the destination along the preset route.[4,5,6]
This section explains the validity of the growingon-demand self-organizing neural network through online continuous learning and generating route map in campus environment
Summary
Human beings and animals exhibit superior capabilities in spatial cognition and navigation even in a complex largescale environment or without complete and accurate perception information. This method utilizes the growing-on-demand self-organizing neural network to build the route map of an environment through constantly adding new route nodes with the deepening exploration of robot into the environment, so as to form an experience-driven route knowledge learning model and apply it into robot planning and navigation.
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