A Two-Level Approach to Motion Planning of Soft Variable-Length Manipulators

Soft variable-length continuum manipulators have emerged as ideal agents in common human interaction scenarios owing to their flexible movements, large workspace and safety assurance. Besides, it’s also their variable-length property that leads to a much larger configuration space, which makes it more difficult to solve the motion planning problem using state-of-the-art sampling-based methods. In this paper, we propose an algorithm that fully exploits the variable-length property of these manipulators. The algorithm directly generates and selects feasible configuration nodes in task space. Before that, a path of the manipulator’s end effector is pre-generated in task space according to the positions of the goal and obstacles, which provides approximately accurate guiding direction of the whole manipulator. During the simulation experiments, the two-level algorithm is validated with efficiency in comparison of Jacobian-based methods and other extensional tasks.