Paper Info
Title
Follow-The-Leader Motion Strategy For Multi-Section Continuum Robots Based On Differential Evolution Algorithm
Abstract
PurposeThe purpose of this paper is to present a follow-the-leader motion strategy for multi-section continuum robots, which aims to make the robot have the motion ability in a confined environment and avoid a collision.Design/methodology/approachFirst, the mechanical design of a multi-section continuum robot is introduced and the forward kinematic model is built. After that, the follow-the-leader motion strategy is proposed and the differential evolution (DE) algorithm for calculating optimal posture parameters is presented. Then simulations and experiments are carried out on a series of predefined paths to analyze the performance of the follow-the-leader motion.FindingsThe follow-the-leader motion can be well performed on the continuum robots this study proposes in this research. The experimental results show that the deviation from the path is less than 9.7% and the tip error is no more than 15.6%.Research limitations/implicationsCurrently, the follow-the-leader motion is affected by the following factors such as gravity and continuum robot design. Furthermore, the position error is not compensated under open-loop control. In future work, this paper will improve the accuracy of the robot and introduce a closed-loop control strategy to improve the motion accuracy.Originality/valueThe main contribution of this paper is to present an algorithm to generate follow-the-leader motion of the continuum robot based on DE. This method is suitable for solving new arrangements in the process of following a nonlinear path. Then, it is expected to promote the engineering application of the continuum robot.
Year
DOI
Venue
2021
10.1108/IR-01-2021-0001
INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION
Keywords
DocType
Volume
Continuum robot, Differential evolution algorithm, Follow-the-leader, Motion strategy, Multi-section
Journal
48
Issue
ISSN
Citations 
4
0143-991X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Guohua Gao131.79
Pengyu Wang200.34
Hao Wang3440127.79