Research on collision avoidance system for four-wheeled robot based on multi-sensor fusion

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Publicado en:Journal of Physics: Conference Series vol. 2990, no. 1 (Apr 2025), p. 012029
Autor principal: Luo, Jingyi
Otros Autores: Zhang, Yongjie
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IOP Publishing
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Acceso en línea:Citation/Abstract
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024 7 |a 10.1088/1742-6596/2990/1/012029  |2 doi 
035 |a 3191138245 
045 2 |b d20250401  |b d20250430 
100 1 |a Luo, Jingyi  |u Guangxi Vocational Normal University , No. 17, Luowen Avenue, Xixiangtang District, Nanning, Guangxi, China; Key Laboratory of Application Technology of Intelligent Connected Vehicle ( Guangxi Vocational Normal University ), Education Department of Guangxi Zhuang Autonomous Region, Guangxi, China 
245 1 |a Research on collision avoidance system for four-wheeled robot based on multi-sensor fusion 
260 |b IOP Publishing  |c Apr 2025 
513 |a Journal Article 
520 3 |a This paper investigates a collision avoidance system for four-wheeled robots based on multi-sensor fusion, aiming to improve the robustness of the robot in complex and variable environments. To address the problem of system performance degradation due to a single sensor’s failure, this paper adopts multi-sensor data fusion technology, which integrates multiple data sources such as visual sensors and LiDAR, to perceive the environment comprehensively. At the same time, the data processing and decision-making algorithms are optimized in conjunction with robust design techniques to ensure that the system can continue to operate stably in the face of uncertainty and unexpected situations. In this study, PreScan is used to conduct simulation and modeling experiments, and the results show that the system effectively enhances the fault tolerance and robustness of the four-wheeled robot, thus providing substantial support for the safe implementation of robotics. 
653 |a Robotics 
653 |a Algorithms 
653 |a Data integration 
653 |a Robots 
653 |a Collision avoidance 
653 |a Data processing 
653 |a Performance degradation 
653 |a Robust design 
653 |a Multisensor fusion 
653 |a Fault tolerance 
700 1 |a Zhang, Yongjie  |u Guangxi Vocational Normal University , No. 17, Luowen Avenue, Xixiangtang District, Nanning, Guangxi, China; Key Laboratory of Application Technology of Intelligent Connected Vehicle ( Guangxi Vocational Normal University ), Education Department of Guangxi Zhuang Autonomous Region, Guangxi, China 
773 0 |t Journal of Physics: Conference Series  |g vol. 2990, no. 1 (Apr 2025), p. 012029 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3191138245/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3191138245/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch