Trajectory planning and tracking control of a small magnetic soft crawling robot for applications in complex environments

Magnetic soft robots have recently attracted widespread attention and show great potential for biomedical applications. However, research on their navigation and control in workspaces such as the human body and microchips remains relatively limited, while practical applications require precise closed-loop control. Therefore, using a small magnetic soft crawling robot as an example, this paper develops a trajectory planning and controller design scheme to achieve autonomous motion in complex environments. Firstly, a trajectory planning algorithm combining an improved ant colony optimization (ACO) algorithm with the dynamic window approach (DWA) is proposed. The improved ACO algorithm is presented here to perform global path planning in environments with known obstacles. The DWA is employed to carry out the local trajectory planning to achieve avoidance of unknown obstacles in complex environments. An intelligent proportional-integral (iPI)-based sliding mode controller is then designed to achieve high-precision trajectory tracking using visual feedback, providing a new approach for the precise control of magnetic soft robots in complex environments. Two experiments are conducted on the experimental platform, and the results verify that the proposed method is effective.