Journal Published Online: 27 June 2022
Volume 6, Issue 1

Movement Trajectory Control of an Intelligent Mobile Robot Controlled by Machine Vision

CODEN: SSMSCY

Abstract

Intelligent mobile robots can take up various repetitive labor tasks instead of humans, but with the increase in labor requirements, the requirements for the autonomous working ability of intelligent mobile robots have also increased. This research used machine vision to identify obstacles in the working environment; then, a plane model was constructed for the working environment of the intelligent mobile robot with the help of machine vision. The moving path was planned by the ant colony algorithm. The robot adopted the trajectory tracking control law to track the planned path to realize the autonomous movement of the intelligent mobile robot. Finally, experiments were carried out, and the trajectory control of the robot moving on the path planned by the ant colony algorithm was compared with that planned by the genetic algorithm. The results showed that the ant colony algorithm converged faster and planned a shorter path after stability; when the coordinates of the starting and end points of the planned path were (0.0 m, 2.5 m) and (5.0 m, 0.0 m), respectively, the turning point coordinates of the paths planned by the genetic and ant colony algorithms were (2.5 m, 2.0 m) and (3.5 m, 0.0 m), respectively; the robot had shorter movement trajectory, smaller deviation, and less movement time on the path planned by the ant colony algorithm than the genetic algorithm.

Author Information

Sun, Jumei
School of Intelligent Manufacturing, Changzhou Vocational Institute of Engineering, Changzhou, Jiangsu, China
Chu, Qin
School of Intelligent Manufacturing, Changzhou Vocational Institute of Engineering, Changzhou, Jiangsu, China
Liu, Shukai
School of Intelligent Manufacturing, Changzhou Vocational Institute of Engineering, Changzhou, Jiangsu, China
Pages: 9
Price: Free
Related
Reprints and Permissions
Reprints and copyright permissions can be requested through the
Copyright Clearance Center
Details
Stock #: SSMS20220010
ISSN: 2520-6478
DOI: 10.1520/SSMS20220010