Adaptive Path-Planning for Autonomous Robots: A UCH-Enhanced Q-Learning Approach

With the rapid development of the combination of control technology and the Artificial Intelligence(AI) field, the intelligent control of mobile robots and their applications like industrial manufacturing, logistics sorting, etc. in this field is evolving towards self-learning and adaptation [1]. For example, intelligent control of mobile robots in complex environments can autonomously move in various environments without external assistance [2], which requires navigation [3] and motion planning-related technologies in practical applications. Motion planning is divided into path planning and trajectory planning [4]. Path planning often serves as the crucial step of trajectory planning, its goal is to find the optimal path from a starting point to an endpoint in a given environment. However, path planning in dynamic environments is more practical and challenging [5].