Feasible Static Workspace Optimization of Tendon Driven Continuum Robot based on Euclidean norm

TL;DR

This paper presents a genetic algorithm-based method to optimize tendon forces in a two-segment tendon-driven continuum robot, maximizing its feasible static workspace under external loads, which enhances design efficiency.

Contribution

It introduces a novel optimization approach using Euclidean norm maximization for the static workspace of a tendon-driven continuum robot, considering external forces and torques.

Findings

Effective identification of optimal tendon forces

Maximized static workspace under external loads

Validated approach through simulation results

Abstract

This paper focuses on the optimal design of a tendon-driven continuum robot (TDCR) based on its feasible static workspace (FSW). The TDCR under consideration is a two-segment robot driven by eight tendons, with four tendon actuators per segment. Tendon forces are treated as design variables, while the feasible static workspace (FSW) serves as the optimization objective. To determine the robot's feasible static workspace, a genetic algorithm optimization approach is employed to maximize a Euclidian norm of the TDCR's tip position over the workspace. During the simulations, the robot is subjected to external loads, including torques and forces. The results demonstrate the effectiveness of the proposed method in identifying optimal tendon forces to maximize the feasible static workspace, even under the influence of external forces and torques.