Goal-Oriented Control Strategies for Soft Growing Robots

Abstract

Soft growing robots, as highly mobile pneumatic membrane robots, are limited in control performance due to their soft structure and nonlinear mechanical properties, especially under dynamic conditions. Therefore, developing reliable control strategies for the robot is essential. This study proposes a dual-thread, goal-oriented control strategy for soft growing robot that combines planning and control. By integrating graph convolutional networks with deep reinforcement learning, the global path planning method is better suited to the self-growing behaviors of soft robots, leading to improvements in both computational efficiency and accuracy compared to inverse kinematics planning methods. Motion control reduces the adverse effects of deformation errors caused by its own low stiffness or by disturbances in the external environment. This strategy effectively combines reinforcement learning-based global planning with a multiple closed-loop motion control system, addressing the issues of low precision and reliability under dynamic conditions. Experimental results demonstrate that the robot achieves a tracking accuracy of 11.83 mm within a 5-meter range and successfully tracks and approaches a non-cooperative dynamic target. These results highlight the significant potential of the proposed approach in applications such as target capture and dynamic manipulation.