Novel Contact Modeling for High Aspect Ratio Soft Robots

Contact modeling between a soft robot and its environment is challenging due to soft robots' compliance and the difficulty of embedding sensors. Current modeling methods are computationally expensive and require highly accurate material characterization to produce useful results. In this article, we present a contact model that utilizes linear complementarity and Hencky bar-chain methods, and requires only static images to efficiently predict the interaction between actuator and environment. These methods have yet to be introduced to the soft robotics community for modeling robots that deform due to eigenstrains or strains not caused by external forces. We validated our model using a custom experimental setup and computer vision algorithm on 3-mm OD, 90-mm long, tube-like actuators. Our results indicated a 1.06% difference in shape between model and experiment, with computation times in 10 s of ms-three to four orders of magnitude faster than nonlinear gradient descent. Additionally, the error in interaction forces between the model and experiment decreased as pressure increased, with an average error magnitude of 45% and 21% for pressures at the low and high ends of the tested range, respectively.