An Optimization-Based Inverse Kinematics Solver for Continuum Manipulators in Intricate Environments

TL;DR

This paper introduces an optimization-based inverse kinematics solver for continuum manipulators that efficiently handles obstacle avoidance and complex constraints in intricate environments, outperforming existing methods in flexibility and computational efficiency.

Contribution

The paper presents a novel IK solver specifically designed for high DoF continuum manipulators in complex environments, integrating obstacle avoidance and constraints with improved efficiency.

Findings

Demonstrates superior flexibility and robustness in simulations

Shows efficient computation with increasing DoF

Performs well in highly unstructured workspaces

Abstract

Continuum manipulators have gained significant attention as a promising alternative to rigid manipulators, offering notable advantages in terms of flexibility and adaptability within intricate workspace. However, the broader application of high degree-of-freedom (DoF) continuum manipulators in intricate environments with multiple obstacles necessitates the development of an efficient inverse kinematics (IK) solver specifically tailored for such scenarios. Existing IK methods face challenges in terms of computational cost and solution guarantees for high DoF continuum manipulators, particularly within intricate workspace that obstacle avoidance is needed. To address these challenges, we have developed a novel IK solver for continuum manipulators that incorporates obstacle avoidance and other constraints like length, orientation, etc., in intricate environments, drawing inspiration from optimization-based path planning methods. Through simulations, our proposed method showcases superior flexibility, efficiency with increasing DoF, and robust performance within highly unstructured workspace, achieved with acceptable latency.