Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models
This work tackles the problem of designing and controlling underwater soft robots for applications like marine exploration, though it is incremental with preliminary models.
The researchers developed a modular, submersible soft robotic arm with 3D-printable parts and hydraulic actuators to address challenges in underwater soft robotics, and created preliminary deep neural network models for forward and inverse kinematics as a step toward machine learning control.
Many soft-body organisms found in nature flourish underwater. Similarly, soft robots are potentially well-suited for underwater environments partly because the problematic effects of gravity, friction, and harmonic oscillations are less severe underwater. However, it remains a challenge to design, fabricate, waterproof, model, and control underwater soft robotic systems. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics and mechanical elements. This work presents the development of a modular and submersible soft robotic arm driven by hydraulic actuators which consists of mostly 3D printable parts which can be assembled or modified in a relatively short amount of time. Its modular design enables multiple shape configurations and easy swapping of soft actuators. As a first step to exploring machine learning control algorithms on this system, we also present preliminary forward and inverse kinematics models developed using deep neural networks.