Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

Abstract

Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control methods. In this paper, a novel biomimetic soft robot driven by Shape Memory Alloy (SMA) with light weight and multi-motion abilities is introduced. We adapt deep learning to perceive irregular targets in an unstructured environment. Aiming at the target searching task, an intelligent visual servo control algorithm based on Q-learning is proposed to generate distance-directed end effector locomotion. In particular, a threshold reward system for the target searching task is proposed to enable a certain degree of tolerance for pointing errors. In addition, the angular velocity and working space of the end effector with load and without load based on the established coupling kinematic model are presented. Our framework enables the trained soft robot to take actions and perform target searching. Realistic experiments under different conditions demonstrate the convergence of the learning process and effectiveness of the proposed algorithm.