Path Planning in Dynamic Environments Based on Q-Learning

Abstract

With the rapid progress of science and technology, the scope of applications for mobile robots is growing. Path planning in dynamic environment is always a challenging task for mobile robot, which shows significant impacts in the medical field and the military field. Q-learning, a model-free reinforcement learning algorithm, can recognize its surroundings and demonstrate a system making decisions for itself about how this algorithm learns to make accurate decisions about achieving optimal target. Therefore, a path planning algorithm was proposed for robots in dynamic environments based on Q-learning. This algorithm can successfully generate several viable paths in environments with both static obstacles, dynamic obstacles and target point. First, three environments with different levels of complexity were created. Afterwards, to generate several optimal paths, the path planning algorithm was conducted in multiple times in different environments. Finally, the experimental results were collected and visualized to illustrate information. The effectiveness of the proposed algorithm is validated by experiment results in solving problems of path planning in dynamic environments with required point.