Rajendran Sunil Kumar, Zhang Feitian
Department of Electrical and Computer Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, United States.
Department of Advanced Manufacturing and Robotics, Peking University, Beijing, China.
Front Robot AI. 2022 Mar 4;8:809427. doi: 10.3389/frobt.2021.809427. eCollection 2021.
A rapidly growing field of aquatic bio-inspired soft robotics takes advantage of the underwater animals' bio-mechanisms, where its applications are foreseen in a vast domain such as underwater exploration, environmental monitoring, search and rescue, oil-spill detection, etc. Improved maneuverability and locomotion of such robots call for designs with higher level of biomimicry, reduced order of complex modeling due to continuum elastic dynamics, and challenging robust nonlinear controllers. This paper presents a novel design of a soft robotic fish actively actuated by a newly developed kind of artificial muscles-super-coiled polymers (SCP) and passively propelled by a caudal fin. Besides SCP exhibiting several advantages in terms of flexibility, cost and fabrication duration, this design benefits from the SCP's significantly quicker recovery due to water-based cooling. The soft robotic fish is approximated as a 3-link representation and mathematically modeled from its geometric and dynamic perspectives to constitute the combined system dynamics of the SCP actuators and hydrodynamics of the fish, thus realizing two-dimensional fish-swimming motion. The nonlinear dynamic model of the SCP driven soft robotic fish, ignoring uncertainties and unmodeled dynamics, necessitates the development of robust/intelligent control which serves as the motivation to not only mimic the bio-mechanisms, but also mimic the cognitive abilities of a real fish. Therefore, a learning-based control design is proposed to meet the yaw control objective and study its performance in path following various swimming patterns. The proposed learning-based control design employs the use of deep-deterministic policy gradient (DDPG) reinforcement learning algorithm to train the agent. To overcome the limitations of sensing the soft robotic fish's states by designing complex embedded sensors, overhead image-based observations are generated and input to convolutional neural networks (CNNs) to deduce the curvature dynamics of the soft robot. A linear quadratic regulator (LQR) based multi-objective reward is proposed to reinforce the learning feedback of the agent during training. The DDPG-based control design is simulated and the corresponding results are presented.
一个快速发展的水生生物启发式软机器人领域利用了水下动物的生物机制，其应用预计将涵盖水下探索、环境监测、搜索救援、溢油检测等广泛领域。此类机器人机动性和运动能力的提升需要更高水平的仿生设计、由于连续体弹性动力学而降低的复杂建模阶次以及具有挑战性的鲁棒非线性控制器。本文提出了一种新型软机器人鱼的设计，它由一种新开发的人工肌肉——超卷曲聚合物（SCP）主动驱动，并由尾鳍被动推进。除了SCP在柔韧性、成本和制造时间方面具有诸多优势外，该设计还受益于SCP基于水冷却的显著更快的恢复速度。软机器人鱼被近似为一个三连杆模型，并从几何和动力学角度进行数学建模，以构成SCP致动器的组合系统动力学和鱼的流体动力学，从而实现二维鱼游运动。忽略不确定性和未建模动力学的情况下，SCP驱动的软机器人鱼的非线性动态模型需要开发鲁棒/智能控制，这不仅是模仿生物机制的动机，也是模仿真实鱼类认知能力的动机。因此，提出了一种基于学习的控制设计，以实现偏航控制目标并研究其在各种游泳模式下路径跟踪中的性能。所提出的基于学习的控制设计采用深度确定性策略梯度（DDPG）强化学习算法来训练智能体。为了克服通过设计复杂的嵌入式传感器来感知软机器人鱼状态的局限性，生成基于头顶图像的观测数据并输入到卷积神经网络（CNN）中，以推断软机器人的曲率动力学。提出了一种基于线性二次调节器（LQR）的多目标奖励，以在训练期间强化智能体的学习反馈。对基于DDPG的控制设计进行了仿真，并给出了相应结果。
