Joint Elasticity Produces Energy Efficiency in Underwater Locomotion

Joint Elasticity Produces Energy Efficiency in Underwater Locomotion: Verification with Deep Reinforcement Learning

Underwater snake robots have received attention because of their unique mechanics and locomotion patterns. Given their highly redundant degrees of freedom, designing energy-efficient gait has been a main challenge for long-term autonomy of underwater snake robots. We propose a gait design method for an underwater snake robot based on deep reinforcement learning and curriculum learning. For comparison, we consider gait generated by a conventional parametric gait equation controller as the baseline. Furthermore, inspired by the joints of living organisms, we consider elasticity (stiffness) in the joints of the snake robot to verify whether it contributes to the generation of energy-efficiency in underwater gait.
We first demonstrate that the deep reinforcement learning controller can produce a more energy-efficient gait than the gait equation controller in underwater locomotion, by finding the control patterns which maximize the effect of energy-efficiency through the exploitation of joint elasticity.
In addition, appropriate joint elasticity can increase the maximum velocity achievable by a snake robot. Finally, simulation results in different liquid environments confirm that the deep reinforcement learning controller is superior to the gait equation controller and it can find adaptive energy efficient motion even the liquid environment is changed.

Видео Joint Elasticity Produces Energy Efficiency in Underwater Locomotion канала Neuro-Robotics Lab