S-RRT*-based Obstacle Avoidance Autonomous Motion Planner for Continuum-rigid Manipulator

TL;DR

The paper tackles obstacle-aware, smooth trajectory planning for a high-DOF continuum-rigid manipulator. It integrates inverse instantaneous kinematics (IIK) with the S-$\text{RRT}^*$ algorithm and extends null-space-based obstacle avoidance to continuum arms, supported by a dedicated closest-point Jacobian calculation for both continuum and rigid segments. Key contributions include a fast method to compute the closest point and its Jacobian, an extension of null-space avoidance to continuum arms with joint-limit handling, and a full S-$\text{RRT}^*$-based planner that achieves obstacle avoidance with high-quality end-effector paths and lower computation time. The simulation results demonstrate effective collision avoidance and path tracking with superior timing stability compared to comparable IIK-based methods, indicating strong potential for practical deployment in surgery and complex manipulation tasks. Future work targets dynamic environments and real-world experiments.

Abstract

Continuum robots are compact and flexible, making them suitable for use in the industries and in medical surgeries. Rapidly-exploring random trees (RRT) are a highly efficient path planning method, and its variant, S-RRT, can generate smooth feasible paths for the end-effector. By combining RRT with inverse instantaneous kinematics (IIK), complete motion planning for the continuum arm can be achieved. Due to the high degrees of freedom of continuum arms, the null space in IIK can be utilized for obstacle avoidance. In this work, we propose a novel approach that uses the S-RRT* algorithm to create paths for the continuum-rigid manipulator. By employing IIK and null space techniques, continuous joint configurations are generated that not only track the path but also enable obstacle avoidance. Simulation results demonstrate that our method effectively handles motion planning and obstacle avoidance while generating high-quality end-effector paths in complex environments. Furthermore, compared to similar IIK methods, our approach exhibits superior computation time.

Figures (7)

• Figure 1: (a) Prototype whole part of manipulator and actuator, (b) prototype of continuum-rigid forceps manipulator.
• Figure 2: (a) Continuum rigid arm model by constant curvature ($\lambda_i = L_s/\theta_i$) and (b) wire arrangement.
• Figure 3: (a) Calculate the closest point in the continuum and (b) rigid arm.
• Figure 4: Motion planning with and without obstacle avoidance for a fixed circle path. The $\boldsymbol{\mu}$ refer to the $\boldsymbol{\mu}$ in the Eq.\ref{['weight_jacob']}.
• Figure 5: Quantitative analysis of fixed path motion planning with two planners (a) Error between the end-effector location and the expected path, (b) Minimum distance to the obstacle at each step for both planners, (c) Location of the closest point at each step for the obstacle avoidance planner. (C refer to continuum arm, R refer to rigid arm and numbers refer to their index.)