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

2026-02-06

AI Summary

This study addresses the limited operational capability of tendon-driven continuum robots under external loads by proposing a novel optimization approach that treats tendon forces as design variables and maximizes the Euclidean norm of the end-effector position to enlarge the feasible static workspace (FSW). A static mechanical model of a two-segment, eight-tendon-driven continuum robot is developed, and an efficient genetic algorithm is employed to determine the optimal tendon force configuration. The method explicitly accounts for external forces and moments, significantly expanding the robot’s static workspace compared to conventional approaches. This work provides a new framework for enhancing the manipulation performance of continuum robots in complex loading environments.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• tendon-driven continuum robot
• feasible static workspace
• workspace optimization
• Euclidean norm
• external loads

Innovation

• tendon-driven continuum robot
• feasible static workspace
• Euclidean norm
• genetic algorithm
• workspace optimization