Li Guanda, Shintake Jun, Hayashibe Mitsuhiro
Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Chofu, Japan.
Front Robot AI. 2023 Feb 9;10:1102854. doi: 10.3389/frobt.2023.1102854. eCollection 2023.
Recently, soft robotics has gained considerable attention as it promises numerous applications thanks to unique features originating from the physical compliance of the robots. Biomimetic underwater robots are a promising application in soft robotics and are expected to achieve efficient swimming comparable to the real aquatic life in nature. However, the energy efficiency of soft robots of this type has not gained much attention and has been fully investigated previously. This paper presents a comparative study to verify the effect of soft-body dynamics on energy efficiency in underwater locomotion by comparing the swimming of soft and rigid snake robots. These robots have the same motor capacity, mass, and body dimensions while maintaining the same actuation degrees of freedom. Different gait patterns are explored using a controller based on grid search and the deep reinforcement learning controller to cover the large solution space for the actuation space. The quantitative analysis of the energy consumption of these gaits indicates that the soft snake robot consumed less energy to reach the same velocity as the rigid snake robot. When the robots swim at the same average velocity of 0.024 m/s, the required power for the soft-body robot is reduced by 80.4% compared to the rigid counterpart. The present study is expected to contribute to promoting a new research direction to emphasize the energy efficiency advantage of soft-body dynamics in robot design.
最近，软机器人技术备受关注，因为其源自机器人物理柔顺性的独特特性预示着众多应用前景。仿生水下机器人是软机器人技术中一个很有前景的应用领域，有望实现与自然界真实水生生物相当的高效游动。然而，这类软机器人的能量效率此前并未受到太多关注，也未得到充分研究。本文通过比较软质和硬质蛇形机器人的游动情况，开展了一项对比研究，以验证软质身体动力学对水下运动能量效率的影响。这些机器人具有相同的电机容量、质量和身体尺寸，同时保持相同的驱动自由度。使用基于网格搜索的控制器和深度强化学习控制器探索不同的步态模式，以覆盖驱动空间的大解决方案空间。对这些步态的能量消耗进行定量分析表明，软质蛇形机器人在达到与硬质蛇形机器人相同速度时消耗的能量更少。当机器人以0.024米/秒的相同平均速度游动时，软质身体机器人所需的功率比硬质机器人降低了80.4%。本研究有望推动一个新的研究方向，即在机器人设计中强调软质身体动力学的能量效率优势。
