Abstract
This paper proposes a goal-directed locomotion method for a snake-shaped robot in 3D complex environment based on path-integral reinforcement learning. This method uses a model-free online Q-learning algorithm to evaluate action strategies and optimize decision-making through repeated “exploration-learning-utilization” processes to complete snake-shaped robot goal-directed locomotion in 3D complex environment. The proper locomotion control parameters such as joint angles and screw-drive velocities can be learned by path-integral reinforcement learning, and the learned parameters were successfully transferred to the snake-shaped robot. Simulation results show that the planned path can avoid all obstacles and reach the destination smoothly and swiftly.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.