The Technology Statues and Trend of Path Planning of Logistics Robot

Research Article
Open access

The Technology Statues and Trend of Path Planning of Logistics Robot

Xiaoying Li 1
  • 1 College of Engineering, China Agricultural University, Beijing, China, 100091    
  • *corresponding author
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220595
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

In recent years, the application of logistics robots has become an important means for logistics enterprises to compete in the market. Using logistics robots to pick or transport goods can effectively improve storage efficiency, and how to obtain the optimal path for logistics robots are the focuses of research. This paper introduces the research status and algorithm of logistics robot path planning, and analyzes the research trend and direction of logistics robot path planning technology according to different research. Automated navigation vehicles in logistics robots are developing rapidly, and the safety and efficiency of effective cooperation between AGS have been greatly improved. In terms of algorithms, the main algorithms currently used are A*, Q-learning and genetic algorithm, and the robot has achieved global path planning and path planning in a dynamic environment. At present, the main objective of scholars in robot path planning is to find the shortest walking path in the shortest time. However, in a diversified environment, the objective of robot path planning will be more specific and more realistic.

Keywords:

Li,X. (2023). The Technology Statues and Trend of Path Planning of Logistics Robot. Applied and Computational Engineering,2,893-898.
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References

[1]. LIU jingyi. Research and implementation of Multi-AGV path Planning in Automated Warehouse Scheduling System [D]. University of Chinese Academy of Sciences (Shenyang Institute of Computing Technology, Cas),2018.

[2]. Meng Chong,Ren yu. Multiple AGV scheduling based on multiple population genetic algorithm [J]. Journal of electronic science and technology, 2018, 31(11): 50 + 47-68. The DOI: 10.16180 / j.carol carroll nki issn1007-7820.2018.11.012.

[3]. Yi Z, Wang F, Fu F, et al. Multi-AGV path planning for indoor factory by using prioritized planning and improved ant algorithm[J]. Journal of Engineering and Technological Sciences, 2018, 50(4):534-547.

[4]. Voorhies R C , Elazary L , Ii D F P . Optimizing robotic movements based on an autonomous coordination of resources amongst robots:, US10725462B2[P]. 2020.

[5]. Pandey A . Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review[J]. International Journal of Robotics and Automation, 2017, 2(3):1-12.

[6]. Shen Bowen, Yu Ningbo, Liu Jingtai. Journal of intelligent systems,2014,9(6):659-664

[7]. Xu Yuanzheng. Research on Path Planning Algorithm in Multi-Mobile Robot System [D]. University of Electronic Science and Technology of China.

[8]. WANG Xiuhong, LIU Xuehao, WANG Yongcheng. A Research on Task Scheduling and Path Planning of Mobile Robot in Warehouse Logistics Based on Improved A* algorithm[J]. Industrial Engineering Journal, 2019, 22(6): 34-39.

[9]. Huang Baiyue. Research and Application of Multi-AGV Path Planning in Warehouse Logistics [D]. Chongqing: Chongqing University of Posts and Telecommunications,2019.

[10]. Wang Hongbin, Hao Ce, Zhang Ping, et al. Path Planning for Mobile Robots Based on A* Algorithm and Artificial Potential Field Method. China Mechanical Engineering, 2019, 30(20):8.

[11]. Chen Mingzhi, Qian Tonghui, Zhang Shizhen, et al. Modern electronics technique,2019,42(22):174-177,182. DOI:10.16652/j.issn.1004-373x.2019.22.037.

[12]. Elshamli A, Abdullah H A, Areibi S. Genetic algorithm for dynamic path planning[C]// Conference on Electrical & Computer Engineering. IEEE, 2004.

[13]. Tuncer A, Yildirim M. Dynamic path planning of mobile robots with improved genetic algorithm[J]. Computers & Electrical Engineering, 2012, 38(6):1564–1572.

[14]. Han Z , Wang D, Liu F, et al. Multi-AGV path planning with double-path constraints by using an improved genetic algorithm[J]. Plos One, 2017, 12(7):e0181747.

Cite this article

Li,X. (2023). The Technology Statues and Trend of Path Planning of Logistics Robot. Applied and Computational Engineering,2,893-898.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. LIU jingyi. Research and implementation of Multi-AGV path Planning in Automated Warehouse Scheduling System [D]. University of Chinese Academy of Sciences (Shenyang Institute of Computing Technology, Cas),2018.

[2]. Meng Chong,Ren yu. Multiple AGV scheduling based on multiple population genetic algorithm [J]. Journal of electronic science and technology, 2018, 31(11): 50 + 47-68. The DOI: 10.16180 / j.carol carroll nki issn1007-7820.2018.11.012.

[3]. Yi Z, Wang F, Fu F, et al. Multi-AGV path planning for indoor factory by using prioritized planning and improved ant algorithm[J]. Journal of Engineering and Technological Sciences, 2018, 50(4):534-547.

[4]. Voorhies R C , Elazary L , Ii D F P . Optimizing robotic movements based on an autonomous coordination of resources amongst robots:, US10725462B2[P]. 2020.

[5]. Pandey A . Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review[J]. International Journal of Robotics and Automation, 2017, 2(3):1-12.

[6]. Shen Bowen, Yu Ningbo, Liu Jingtai. Journal of intelligent systems,2014,9(6):659-664

[7]. Xu Yuanzheng. Research on Path Planning Algorithm in Multi-Mobile Robot System [D]. University of Electronic Science and Technology of China.

[8]. WANG Xiuhong, LIU Xuehao, WANG Yongcheng. A Research on Task Scheduling and Path Planning of Mobile Robot in Warehouse Logistics Based on Improved A* algorithm[J]. Industrial Engineering Journal, 2019, 22(6): 34-39.

[9]. Huang Baiyue. Research and Application of Multi-AGV Path Planning in Warehouse Logistics [D]. Chongqing: Chongqing University of Posts and Telecommunications,2019.

[10]. Wang Hongbin, Hao Ce, Zhang Ping, et al. Path Planning for Mobile Robots Based on A* Algorithm and Artificial Potential Field Method. China Mechanical Engineering, 2019, 30(20):8.

[11]. Chen Mingzhi, Qian Tonghui, Zhang Shizhen, et al. Modern electronics technique,2019,42(22):174-177,182. DOI:10.16652/j.issn.1004-373x.2019.22.037.

[12]. Elshamli A, Abdullah H A, Areibi S. Genetic algorithm for dynamic path planning[C]// Conference on Electrical & Computer Engineering. IEEE, 2004.

[13]. Tuncer A, Yildirim M. Dynamic path planning of mobile robots with improved genetic algorithm[J]. Computers & Electrical Engineering, 2012, 38(6):1564–1572.

[14]. Han Z , Wang D, Liu F, et al. Multi-AGV path planning with double-path constraints by using an improved genetic algorithm[J]. Plos One, 2017, 12(7):e0181747.

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