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Mobile robot tracking control based on lightweight network

Published online by Cambridge University Press:  19 March 2025

Yiming Hua ,
Xueyou Huang ,
Haoxiang Li  and
Xiang Cao Open the ORCID record for Xiang Cao [Opens in a new window]
Yiming Hua
Affiliation:
School of Artificial Intelligence, Anhui University, Hefei, 230601, China Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, China Anhui Provincial Key Laboratory of Security Artificial Intelligence, Hefei, 230601, China
Xueyou Huang
Affiliation:
School of Artificial Intelligence, Anhui University, Hefei, 230601, China Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, China Anhui Provincial Key Laboratory of Security Artificial Intelligence, Hefei, 230601, China
Haoxiang Li
Affiliation:
School of Artificial Intelligence, Anhui University, Hefei, 230601, China Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, China Anhui Provincial Key Laboratory of Security Artificial Intelligence, Hefei, 230601, China
Xiang Cao*
Affiliation:
School of Artificial Intelligence, Anhui University, Hefei, 230601, China Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, China Anhui Provincial Key Laboratory of Security Artificial Intelligence, Hefei, 230601, China
*
Corresponding author: Xiang Cao; Email: cxeffort@126.com
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Abstract

Target tracking technology is a key research area in the field of mobile robots, with wide applications in logistics, security, autonomous driving, and more. It generally involves two main components: target recognition and target following. However, the limited computational power of the mobile robot’s controller makes achieving high precision and fast target recognition and tracking a challenge. To address the challenges posed by limited computing power, this paper proposes a target-tracking control algorithm based on lightweight neural networks. First, a depthwise separable convolution-based backbone is introduced for feature extraction. Then, an efficient channel attention module is incorporated into the target recognition algorithm to minimize the impact of redundant features and emphasize important channels, thereby reducing model complexity and enhancing network efficiency. Finally, based on the data collected from visual and ultrasonic sensors, a model predictive control strategy is used to achieve target tracking. Validation of the proposed algorithm is conducted using a mobile robot equipped with Raspberry Pi 4B. Experimental results demonstrate that the proposed algorithm achieves rapid target tracking.

Keywords

tracking controltarget detectionlightweight networkRaspberry Pi

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 4 , April 2025 , pp. 1331 - 1349
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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