Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments

TL;DR

This paper presents a novel RRT*-based motion planning method for continuum robots that incorporates safety constraints, enabling efficient and obstacle-avoiding navigation in dynamic environments, validated through simulations and prototype tests.

Contribution

It introduces a safety-constrained RRT* algorithm specifically designed for continuum robots, enhancing autonomous navigation in complex, changing environments.

Findings

Efficient trajectory planning in dynamic environments

Successful obstacle avoidance with safety constraints

Prototype tests confirm practical effectiveness

Abstract

Continuum robots, characterized by their high flexibility and infinite degrees of freedom (DoFs), have gained prominence in applications such as minimally invasive surgery and hazardous environment exploration. However, the intrinsic complexity of continuum robots requires a significant amount of time for their motion planning, posing a hurdle to their practical implementation. To tackle these challenges, efficient motion planning methods such as Rapidly Exploring Random Trees (RRT) and its variant, RRT*, have been employed. This paper introduces a unique RRT*-based motion control method tailored for continuum robots. Our approach embeds safety constraints derived from the robots' posture states, facilitating autonomous navigation and obstacle avoidance in rapidly changing environments. Simulation results show efficient trajectory planning amidst multiple dynamic obstacles and provide a robust performance evaluation based on the generated postures. Finally, preliminary tests were conducted on a two-segment cable-driven continuum robot prototype, confirming the effectiveness of the proposed planning approach. This method is versatile and can be adapted and deployed for various types of continuum robots through parameter adjustments.