Localized online learning-based control of a soft redundant manipulator under variable loading

Soft robots are inherently compliant and manoeuvrable manipulators that can passively adapt to their environment. However, in order to fully make use of their unique properties, accurate control should still be maintained when affected by external loading. Commonly used model-based approaches often have low tolerance to unmodelled loading, resulting in significant error when acted on by them. Therefore, in this study we employ a nonparametric learning-based method that can approximate and update the inverse model of a redundant two-segment soft robot in an online manner. The primary contribution of this work is the application and evaluation of the proposed framework on a redundant soft robot. With the addition of redundancy, a constrained optimization approach is taken to consistently resolve null-space behaviour. Through this control framework, the controller can continuously adapt to unknown external disturbances during runtime and maintain end-effector accuracy. The performance of the control framework was evaluated by tracking of a 3D trajectory with a static tip load, and a variable weight tip load. The results indicate that the proposed controller could effectively adapt to the disturbances and continue to track the desired trajectory accurately.