Design, Modeling, and Visual Learning-Based Control of Soft Robotic Fish Driven by Super-Coiled Polymers.

Sunil Kumar Rajendran,Feitian Zhang

doi:10.3389/frobt.2021.809427

Sunil Kumar Rajendran, Feitian Zhang

Open Access

https://doi.org/10.3389/frobt.2021.809427

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Abstract

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 via 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.

Highlights

The nascent field of bio-inspired robotics has gained a huge popularity over the past 2 decades with numerous designs and developments contributed to the community (Pfeifer et al, 2007; Kim et al, 2013; Shi et al, 2015; Laschi et al, 2016; Christianson et al, 2019; Olsen and Kim, 2019), envisioning their applications in domains such as environmental monitoring, deep-sea exploration, search and rescue, and disaster response (Morgansen et al, 2007; Zheng Chen et al, 2010; Marchese et al, 2014; Phamduy et al, 2015)
Our research focuses on developing a biomimetic underwater soft robotic fish that can self-learn its locomotion to achieve different goals such as regulating its angle of orientation and adapting to variable swimming speeds (Rajendran and Zhang, 2018), which eventually serve as decomposed control tasks for high-level control objectives such as traversing along a planned trajectory and studying fish swarming behavior like schooling and shoaling
Our study showed through simulation that speed control of a one-dimensional robotic fish was successfully done with super-coiled polymers (SCP) actuators using reinforcement learning (Rajendran and Zhang, 2018; Sutton and Barto, 2018)

Summary

Introduction

The nascent field of bio-inspired robotics has gained a huge popularity over the past 2 decades with numerous designs and developments contributed to the community (Pfeifer et al, 2007; Kim et al, 2013; Shi et al, 2015; Laschi et al, 2016; Christianson et al, 2019; Olsen and Kim, 2019), envisioning their applications in domains such as environmental monitoring, deep-sea exploration, search and rescue, and disaster response (Morgansen et al, 2007; Zheng Chen et al, 2010; Marchese et al, 2014; Phamduy et al, 2015). Most of the traditional robotic fish prototypes designed in the past, comprise of two or more serially connected structures (Wen et al, 2012; Zhong et al, 2017), whose coordinated discrete movements result in undulations mimicking one of these swimming styles The body of these robots are structurally constructed using rigid materials such as plastic, metal and glass-fiber (Raj and Thakur, 2016), which increases the rigidity and mass of the robot. To overcome this limitation, over the past demi-decade, researchers have been exploring the usage of soft materials (Lauder et al, 2011) such as silicone rubber/elastomer (Katzschmann et al, 2018), silicone prepolymer (Aubin et al, 2019) and silk hydrogel (Donatelli et al, 2018) to construct the body of the fish robot (Olsen and Kim, 2019). The adoption of such soft materials in the construction of the robotic fish greatly contributes towards mimicking the flexibility of the biological fish body, generating a continuous deformation and streamlined displacement of water

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Conclusion