Toward State-Unsaturation Guaranteed Fault Detection Method in Visual Servoing of Soft Robot Manipulators

This paper puts forward a novel sensor-less fault detection method with only task errors feedback and applies it to visual servoing tasks of soft robot manipulators. The method is developed by introducing a suitably designed endogenous accessory signal (EAS). On the one hand, EAS transforms the change of jacobian matrix led by faults into the change of task errors, which enables the fault to be directly measured and detected; on the other hand, EAS adjusts the state trajectories according to the distance between states and their boundaries, so that state saturation is avoided. To enhance the robustness of the method, we introduce an artificial potential field that keeps the states from the undesired hyperplanes that lead to the loss of effectiveness of the method. Due to the uncalibrated feature point, its coordinates used in control laws and artificial potential filed are unknown. An adaptive algorithm is developed to guarantee the stability of the system and the convergence of the image errors. Experiments are conducted to validate the performance of the proposed method in both healthy and faulty systems.