Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Increasing robotic systems autonomy is a major challenge in ensuring both their performance and ease of application in numerous areas of human activity. The present study attempts to combine several artificial intelligence methods to design self-learning control system for the task of mobile robot motion planning in a complex environment. We propose two main components of the control system: a dynamic path planner based on D* algorithm and terrain type prediction subsystem based on classification trees. Efficiency of both of the subsystems grows with time due to knowledge accumulation, leading the robot to a better maneuvering in the environment filled with obstacles.
Index Terms—autonomous mobile robot, motion planning, graph route planning, classification trees, machine learning
Cite: Sergey V. Manko, Valery M. Lokhin, Sekou A. K. Diane, and Alexander S. Panin, "Autonomous Mobile Robot Self-Learning In Motion Planning Problem," International Journal of Mechanical Engineering and Robotics Research, Vol.4, No. 3, pp. 238-241, July 2015. DOI: 10.18178/ijmerr.4.3.238-241
Copyright © 2015-2021 International Journal of Mechanical Engineering and Robotics Research, All Rights Reserved