Dexterity Analysis Based on Jacobian and Performance Optimization for Multi-Segment Continuum Robots

Abstract

This paper focuses on the performance analysis of multi-segment continuum robots, including reachable workspace and dexterity performance. Since excellent dexterity is an important feature of continuum robots, two local indices inspired by separating robotic Jacobian matrix, namely axiality and angularity dexterity, are introduced to explore the dexterity. Then, a Monte Carlo Method is adopted to simulate the distribution of local dexterity over the workspace. On this basis, the corresponding global indices in axiality and angularity are defined to assess global dexterity performance. To investigate the optimal kinematic performance, an objective function related to the segment lengths is designed under the consideration of reachable workspace and dexterity performance. Finally, Particle Swarm Optimization (PSO) algorithm is used to solve this optimization problem successfully. The optimal length distributions for two-segment and three-segment continuum robots are discovered. Most importantly, it is found that our method can also apply to general multi-segment continuum robots.