A Control System Design for an Intelligent Unmanned Automotive

Article A Control System Design for an Intelligent Unmanned Automotive Yundi Yang †, Xin Gao †, Jinwen You, Dengbo Zhang, Zhuo Zhang and Yuanmei Song * School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China * Correspondence: [email protected] † These authors contributed equally to this work. Received: 1 August 2024; Revised: 18 September 2024; Accepted: 28 November 2024; Published: 4 December 2024 Abstract: This study is dedicated to developing a control system for an intelligent unmanned delivery vehicle with rear wheel independent drive. This design includes kinematic analysis based on AT89C51 microcontroller, control software development, arc path interpolation technology and control system circuit design. Typically, a high-precision digital encoder was designed to be installed on the rotating shaft of the motor to produce two pulse signals with a phase difference of 90 degrees, which is essential for determining the motion state of the motor. And the detection of the motor’s rotation direction was achieved through a cleverly designed forward circuit. This system will continuously monitor the driving trajectory concluding from the initially predetermined route and the actual trajectory. As a result, this miniature vehicle is engineered to autonomously navigate and meticulously control its movement trajectory with high precision and efficiency, effortlessly navigating both straight lines and intricate arcs, which propelled a significant enhancement in both the overall level of production automation and the efficiency of manufacturing processes.

Authors

Publication Details

Journal
International Journal of Automotive Manufacturing and Materials
Published
2024-12-04
DOI
https://doi.org/10.53941/ijamm.2024.100024
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.25
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article

A Control System Design for an Intelligent Unmanned Automotive

Dengbo Zhang, Yichen Yang, Xin Gao, Yuanmei Song et al.
1 citations
International Journal of Automotive Manufacturing and Materials
Robotic Path Planning Algorithms
0.25
article

A Control System Design for an Intelligent Unmanned Automotive

Dengbo Zhang, Yichen Yang, Xin Gao, Yuanmei Song, Jinwen You, Zhuo Zhang
article en
1 citations

Abstract

Article A Control System Design for an Intelligent Unmanned Automotive Yundi Yang †, Xin Gao †, Jinwen You, Dengbo Zhang, Zhuo Zhang and Yuanmei Song * School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China * Correspondence: [email protected] † These authors contributed equally to this work. Received: 1 August 2024; Revised: 18 September 2024; Accepted: 28 November 2024; Published: 4 December 2024 Abstract: This study is dedicated to developing a control system for an intelligent unmanned delivery vehicle with rear wheel independent drive. This design includes kinematic analysis based on AT89C51 microcontroller, control software development, arc path interpolation technology and control system circuit design. Typically, a high-precision digital encoder was designed to be installed on the rotating shaft of the motor to produce two pulse signals with a phase difference of 90 degrees, which is essential for determining the motion state of the motor. And the detection of the motor’s rotation direction was achieved through a cleverly designed forward circuit. This system will continuously monitor the driving trajectory concluding from the initially predetermined route and the actual trajectory. As a result, this miniature vehicle is engineered to autonomously navigate and meticulously control its movement trajectory with high precision and efficiency, effortlessly navigating both straight lines and intricate arcs, which propelled a significant enhancement in both the overall level of production automation and the efficiency of manufacturing processes.

International Journal of Automotive Manufacturing and Materials
Industry, innovation and infrastructure
Openalex Percentile: Top 44%
Robotic Path Planning Algorithms
0.25
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