Optimal Path Planning for Autonomous Space Maneuvers based on Reinforcement Q-learning and Cubic Network

This paper proposes a method based on the reinforcement Q-learning to solve the problem of fully autonomous optimal path planning of a space robot. By increasing the number of available satellites in earth’s orbits, designation, and implementation of satellite orbital servicing stations considered by researchers. Nowadays, by considering the advancement in robotics science, space robots could be chosen as a part of solution for maintaining the damaged satellites in earth’s orbits. Guidance, control, and navigation of space robots throughout docking and joint maneuvers need a high degree of precision. In this paper, reinforcement Q-learning algorithm functionality in path planning analyzed through various computational simulations. The finding results from computational simulations demonstrate the usefulness of the mentioned approach.