LAUSR

Soft robots face significant control challenges due to the coupling effects of strain and external forces, particularly with external interactions that change their original model. This paper proposes a vision-based controller integrated with an adaptive algorithm to estimate and compensate for external contact disturbances in such environments. Specifically, the adaptive law can online estimate unknown camera parameters, eliminating the need for costly and environment-specific calibration procedures. In parallel, a contact disturbance estimation strategy is introduced to model and compensate for real-time interaction effects without the foreknowledge of the constitutive equation of the soft mechanism. The adaptive algorithm is seamlessly integrated into the adaptive image-based visual servo (IBVS) controller, allowing simultaneous calibration and contact compensation during task execution. We validated the algorithm on a tendon-driven, octo-articular soft robotic manipulator prototype. The experimental results demonstrated the algorithm’s ability to position the end-effector even under external interactions, with the adaptive parameters converging, thereby validating the effectiveness of the online estimation in assessing the impacts of interactions. Note to Practitioners—The motivation of this article is to address the control issue of soft robots under contacts. The inherent softness and compliance of soft robots endow them with excellent safety interaction performance, making them highly applicable in fields such as medicine, education, and human-robot collaboration. However, in constrained and unknown environments, the kinematic model of soft robots can undergo unpredictable changes due to contact interferences. This poses a significant challenge to the control of soft robots, severely impacting the feasibility of their extensive application. To tackle this problem, this article proposes an adaptive visual servo controller that compensates for contact interferences in real-time, enabling accurate position control of the soft robot’s end-effector during external interactions.