Professor of School of Engineering, Design and Built Environment, 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.
2024-02-24
2024-01-04
2023-11-02
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