Variable-Stiffness Control of A Dual-Segment Soft Robot using Depth Vision

A soft-bodied robot exhibits prominent dexterity due to the soft nature of its material. However, the softness can become a burden when the robot needs to interact with the environment, given that the targeted object is usually much stiffer than the compliant soft robot. A variable-stiffness soft robot, fusing the merits of softness and stiffness, is in favor of many applications, such as robot-assisted minimally invasive surgeries (R-MIS). In this work, we propose a tendon-tensioning method to adaptively control the stiffness of a dual-segment tendon-driven backboneless soft robot based on depth vision. A depth vision-based closed-loop controller is designed for stiffness compensation when the manipulator is subjected to the external load. Experiments were conducted to examine the feasibility and performance of the proposed method. The results confirm our control scheme on the robot with controllability of stiffness up to 132%. Based on our method, the manipulator with an external payload can follow designated trajectories with positioning errors reduced up to 50% comparing to that with open-loop control. Without quantifying the instantaneous stiffness, this work contributes a generalized method for tuning the stiffness of the tendon-driven soft robots in the presence of external disturbances without onboard sensing.