Review of AGV Obstacle Avoidance Algorithm Based on Machine Vision
DOI:
https://doi.org/10.61173/74afjs61Keywords:
AGV trolley, Obstacle avoidance algorithm, Machine learning, Optimization, Potential fieldAbstract
With the development trend of unmanned and intelligent, it has become a development trend for AGV to leave human operators and perform diversified tasks. However, operating in complex environments such as urban streets, mines, and construction sites is a difficult problem that needs to be solved currently. Obstacles in complex environments pose a serious threat to the AGV trolley in operation. Obstacle avoidance technology has become a key part of the AGV trolley’s task decision-making system and plays an important role in ensuring the safe operation of the AGV trolley and improving work efficiency. This article first elaborates on the concept of the AGV trolley obstacle avoidance algorithm and the evaluation criteria for the optimal path, then elaborates and compares the advantages and disadvantages of different types of obstacle avoidance algorithms such as optimization-based, potential field-based, and machine learning-based obstacle avoidance algorithms, and finally obtains the research focus and direction of the AGV trolley.
References
[1] Qiu Guangping. Research on mobile machine vision positioning, navigation and autonomous obstacle avoidance system[D].South China University of Technology,2011.
[2] Felipe Jiménez;;José Eugenio Naranjo.Improving the obstacle detection and identification algorithms of a laserscanner-based collision avoidance system[J].Transportation Research Part C,2010(4).
[3] Miguel Angel Sotelo,Ramon Flores. Vision Based Intelligent System for Autonomous and AssistedDowntown Driving[J]. EUROCAST, 2008,28(3): 326-336.
[4] Seraji,H. A Multisensor Decision Fusion System for Terrain Safety Assessment[J]. IEEETransactions on Robotics, 2009,25(1): 99-108.
[5] Bao Zihan, Li Longhai, Liu Lili, et al. Research on automatic obstacle avoidance technology of rescue robot based on machine vision[J].Machinery Manufacturing and Automation,2024,53(01):202-208.DOI:10.19344/j.cnki. issn1671-5276.2024.01.041.
[6] Yuan Hongbin, Cao Huiqun, Ou Qunyong. Obstacle detection based on laser ranging radar and machine vision[J].Modern Radar,2021,43(05):57-62.DOI:10.16592/ j.cnki.1004-7859.2021.05.009.
[7] Yue Junfeng,LI Xiumei. Automatic tracking and obstacle avoidance system of intelligent car based on machine vision[J]. Journal of Hangzhou Normal University(Natural Science Edition),2020,19(02):200-207.)
[8] LAI Wenpeng,HU Hong. Obstacle avoidance path planning of planar articulated robot based on vision[J].Mechanics & Elect ronics,2020,38(11):71-75+80.)
[9] Wang Tao. Research on surface target detection and obstacle avoidance technology of unmanned boat in inland river channel based on machine vision[D].Harbin University of Commerce,2023.DOI:10.27787/d.cnki.ghrbs.2023.000563.
[10] Zhao Jing, PEI Zinan, JIANG Bin, et al. Visual obstacle avoidance of UAV virtual pipeline based on deep reinforcement learning[J/OL].Acta Automatica Sinica,1-14[2024-07- 20].https://libresource.chd.edu.cn:443/https/443/org/doi/ yitlink/10.16383/j.aas.c230728.
[11] Ying Hao. Research on path tracking and obstacle avoidance of AGV based on vision[D].Kunming University of Science and Technology,2006.
[12] Yao Lijian. Research on vision system and obstacle avoidance planning of robotic arm for eggplant harvesting robot[D].Nanjing Agricultural University,2008.
[13] Cheng Jiayu. Research on motion obstacle detection and obstacle avoidance strategy of agricultural robot based on machine vision[D].Nanjing Agricultural University,2011.
[14] MaliResearch on obstacle avoidance method of mobile robot based on fixed binocular vision[D].Lanzhou University of Technology,2013.
[15] Zheng Haihua. Research on obstacle avoidance of flat ground detection robot based on vision[D].Central South University,2014.
[16] He Jiajian. Obstacle recognition and obstacle avoidance strategy based on binocular vision automatic guided vehicle[D]. Shandong University,2015.
[17] Hou Zhixu. Research on real-time obstacle avoidance of mobile robots in dynamic environment[D].Chongqing University of Technology,2016.
[18] Zhang Yongjun. Research on intelligent mobile system design and obstacle avoidance navigation algorithm based on machine vision[D].Harbin Engineering University,2017.
[19] Chen Shan. Research on binocular visual dynamic obstacle avoidance based on artificial potential field method[D].Hebei University,2019.
[20] Yang Lei,Chen Haihua,Lou Pengyan. Inner Mongolia Science & Technology and Economy,2019,(17):73-75+78.
[21] Liu Lingmin, Hu Jing, Xie Qian. Research on intelligent obstacle avoidance system of picking robot based on computer vision[J].Agricultural Mechanization Re search,2018,40(09):213-217+222.DOI:10.13427/j.cnki. njyi.2018.09.041.
[22] Guo Xiaoyang. Research on obstacle avoidance method of intelligent mobile robot based on machine vision[D].Huazhong University of Science and Technology,2018.
[23] Songyue Yang, Zhijun Meng, Xuzhi Chen, and Ronglei Xie. 2019. Real-time obstacle avoidance with deep reinforcement learning Three-Dimensional Autonomous Obstacle Avoidance for UAV. In Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI ‘19). Association for Computing Machinery, New York, NY, USA, 324–329.
[24] Yue Junfeng,LI Xiumei. Automatic tracking and obstacle Dean&Francis Juncheng Lu avoidance system of intelligent car based on machine vision[J]. Journal of Hangzhou Normal University(Natural Science Edition),2020,19(02):200-207.)
[25] DOI:10.27151/d.cnki.ghnlu.2020.004000. LI Bingyao. Research on intelligent nursing wheelchair bed system based on machine vision navigation and obstacle avoidance[D].South China University of Technology,2020.DOI:10.27151/d.cnki. ghnlu.2020.004000.
[26] Zhou Huiyuan. Research on dynamic obstacle avoidance path planning of mobile robot based on vision[D].Qilu University of Technology,2020.DOI:10.27278/d.cnki. gsdqc.2020.000228.
[27] DOI:10.27322/d.cnki.gsgyu.2020.000420. Jia. Research on dynamic obstacle avoidance system based on binocular stereo vision[D].Shenyang University of Technology,2020. DOI:10.27322/d.cnki.gsgyu.2020.000420.
[28] Xiang Qiqing. Research on dynamic obstacle avoidance path planning of robot based on machine vision[D].Hefei University of Technology,2021.DOI:10.27101/d.cnki. ghfgu.2021.001691.
[29] Liu Yuzhe. Research and implementation of obstacle avoidance algorithm for mobile robot based on machine vision[D].Chongqing University of Posts and Telecommunications,2021.DOI:10.27675/d.cnki. gcydx.2021.000494.
[30] Jianchao Huang, Zhuangzhuang Wang, Zejun Zhang, Yulong He, and Zhiming Cai. 2022. Obstacle detection of indoor mobile robot based on binocular vision. In Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics (ICCIR ‘22). Association for Computing Machinery, New York, NY, USA, 140–145. https://libresource.chd.edu. cn:443/https/443/org/doi/yitlink/10.1145/3548608.3559182
[31] Liu Peng. Research on target detection and obstacle avoidance in field road based on millimeter-wave radar and visual fusion[D].Southwest University,2023.DOI:10.27684/ d.cnki.gxndx.2023.001894.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
