A Control System Design for an Intelligent Unmanned Automotive

Yundi Yang; Xin Gao; Jinwen You; Dengbo Zhang; Zhuo Zhang; Yuanmei Song

doi:10.53941/ijamm.2024.100024

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.

References

1.
Srinivas, C.; Chittaranjan, D.V. Modelling and simulation of multi automated Guided vehicles in a factory layout. Int. J. Sci. Res. Rev. 2014, 2, 177–187.
2.
Wang, R.M.; Zhang, C.T.; Chen, H.; Chen, H.Y.; Li, X.K.; Xu, W.H. Design and practice of intelligent Car based on vision Sensor. Intern. Combust. Engine Accessories 2024, 4, 84–86.
3.
Correa, A.I.; Langevin, A.; Rousseau, L.M. Scheduling and routing of automated guided vehicles: A hybrid approach. Comput. Oper. Res. 2007, 34, 1688–1707.
4.
Bordelon Hoff, E.; Sarker, B.R. An overview of path design and dispatching methods for automated guided vehicles. Integr. Manuf. Syst. 1998, 9, 296–307.
5.
Chen, Z.Y.; Xu, B. AGV path planning based on improved artificial potential field method. In Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications. Shenyang, China, 22–24 January 2021; pp. 32–37.
6.
Yang, R.L.; Wang, J.D.; Chen, R.R. System design of intelligent Obstacle Crossing detection Car based on MCU. Microprocessor 2024, 45, 58–60.
7.
Li, Q.; Xu, W.; Gu, Y.A.; Wang, X.; Hu, P. Design of innovative experimental teaching System based on intelligent car for desert control. Mech. Eng. 2024, 19–23.
8.
Lothar, S.; Lindu, Z. Worldwide development and application of automated guided vehicle systems. Int. J. Serv. Oper. Inf. 2007, 2, 164–176.
9.
Sigal, B.; Edna, S.; Yael, E. Evaluation of automatic guided vehicle systems, Robot. Comput.-Integr. Manuf 2009, 25, 522–528.
10.
Sabattini, L.; Digani, V.; Secchi, C.; Cotena, G.; Ronzoni, D.; Foppoli, M.; Oleari, F. Technological roadmap to boost the introduction of AGVs in industrial applications. In Proceedings of the 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 5–7 September 2013; pp. 203–208.
11.
Han, Q.Q.; Geng, S.Y.; Fu, C.N.; Zhu, X.P. Design of Multi-function Intelligent Car Based on Single-chip Microcomputer Technology. Mech. Eng. Autom. 2024, 160–162.
12.
Wang, H.B. Research on Campus Unmanned Delivery System Based on Mobile Robots. Master’s Thesis, Beijing Jiaotong University, Beijing, China, 2019.
13.
Han, Y.F. Research on Distribution Path Planning of Unmanned Logistics Fleet Based on Deep Reinforcement Learning. Master’s Thesis, Dalian University of Technology, Dalian, China, 2021.
14.
Li, X.; Liu, Y.F.; Wang, H.J. The Impact of Sustainable Development of Cold Chain Logistics on China’s COVID-19 Pandemic. Sustainability. 2022, 14,10358.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us