Title: An obstacle avoidance trajectory control method for robot biomimetic manipulators based on machine vision

Authors: Yongtang Wu

Addresses: School of Artificial Intelligence, Chongqing Three Gorges Vocational College, Wanzhou, 404155, China

Abstract: In order to improve the control effect of obstacle avoidance trajectory, a robot biomimetic robotic arm obstacle avoidance trajectory control method is studied using computer vision technology. By equipping the robot with visual sensors, it can obtain image information from the environment. Based on this information, a region growth algorithm is combined for robotic arm image segmentation, while visual tracking algorithms are used for robotic arm obstacle detection. On the basis of obstacle detection, the convolutional neural network (CNN) in machine vision algorithms is combined to achieve obstacle avoidance trajectory control for robot biomimetic robotic arms. Based on the perceived environmental obstacle information, the obstacle avoidance trajectory is generated. By analysing the experimental results, it can be concluded that the method proposed in this paper can efficiently avoid obstacles of various shapes and sizes, with a high success rate and smoothness, providing strong support for safe and efficient robot operations.

Keywords: machine vision; biomimetic robotic arm; adaptive estimation; region growth algorithm; convolutional neural network; CNN.

DOI: 10.1504/IJMIC.2024.142277

International Journal of Modelling, Identification and Control, 2024 Vol.45 No.2/3, pp.129 - 135

Received: 11 Oct 2023
Accepted: 29 Feb 2024
Published online: 16 Oct 2024 *

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