A cooperative approach to avoiding obstacles and collisions between autonomous industrial vehicles in a simulation platform

被引:5
作者
Grosset, J. [1 ,2 ]
Ndao, A. [1 ]
Fougeres, A-J [1 ]
Djoko-Kouam, M. [1 ,3 ]
Couturier, C. [2 ]
Bonnin, J-M [2 ]
机构
[1] ECAM Rennes Louis Broglie, Bruz, France
[2] IMT Atlantique, IRISA, UMR 6074, Rennes, France
[3] IETR, Cent Supelec, UMR CNRS 6164, Rennes, France
关键词
Autonomous industrial vehicle; vehicle location estimation; vehicle collision avoidance; agent-based simulation; AUTOMATED GUIDED VEHICLE; DIGITAL SUPPLY CHAINS; DECENTRALIZED CONTROL; MULTIAGENT SYSTEM; MANAGEMENT-SYSTEM; TASK ALLOCATION; MOBILE ROBOTS; INTELLIGENT; AVOIDANCE; COORDINATION;
D O I
10.3233/ICA-220694
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Industry 4.0 leads to a strong digitalization of industrial processes, but also a significant increase in communication and cooperation between the machines that make it up. This is the case with autonomous industrial vehicles (AIVs) and other cooperative mobile robots which are multiplying in factories, often in the form of fleets of vehicles, and whose intelligence and autonomy are increasing. While the autonomy of autonomous vehicles has been well characterized in the field of road and road transport, this is not the case for the autonomous vehicles used in industry. The establishment and deployment of AIV fleets raises several challenges, all of which depend on the actual level of autonomy of the AIVs: acceptance by employees, vehicle location, traffic fluidity, collision detection, or vehicle perception of changing environments. Thus, simulation serves to account for the constraints and requirements formulated by the manufacturers and future users of AIVs. In this paper, after having proposed a broad state of the art on the problems to be solved in order to simulate AIVs before proceeding to experiments in real conditions, we present a method to estimate positions of AIVs moving in a closed industrial environment, the extension of a collision detection algorithm to deal with the obstacle avoidance issue, and the development of an agent-based simulation platform for simulating these two methods and algorithms. The resulting/final/subsequent simulation will allow us to experiment in real conditions.
引用
收藏
页码:19 / 40
页数:22
相关论文
共 143 条
  • [81] Liu SM, 2002, INTEGR COMPUT-AID E, V9, P235
  • [82] A RFID-enabled positioning system in automated guided vehicle for smart factories
    Lu, Shaoping
    Xu, Chen
    Zhong, Ray Y.
    Wang, Lihui
    [J]. JOURNAL OF MANUFACTURING SYSTEMS, 2017, 44 : 179 - 190
  • [83] Cooperative Awareness in VANETs: On ETSI EN 302 637-2 Performance
    Lyamin, Nikita
    Vinel, Alexey
    Jonsson, Magnus
    Bellalta, Boris
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (01) : 17 - 28
  • [84] Ma H, 2016, AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS, P1144
  • [85] Everything you need to know about agent-based modelling and simulation
    Macal, C. M.
    [J]. JOURNAL OF SIMULATION, 2016, 10 (02) : 144 - 156
  • [86] Coordination of Autonomous Vehicles: Taxonomy and Survey
    Mariani, Stefano
    Cabri, Giacomo
    Zambonelli, Franco
    [J]. ACM COMPUTING SURVEYS, 2021, 54 (01)
  • [87] Industrial adoption of agent-based technologies
    Marík, V
    McFarlane, D
    [J]. IEEE INTELLIGENT SYSTEMS, 2005, 20 (01) : 27 - 35
  • [88] Marino D, 2011, Arxiv, DOI arXiv:1101.2270
  • [89] A multi-agent system for managing adverse weather situations on the road network
    Marti, I.
    Tomas, V. R.
    Garcia, L. A.
    Martinez, J. J.
    [J]. INTEGRATED COMPUTER-AIDED ENGINEERING, 2010, 17 (02) : 145 - 155
  • [90] Validation of decision-making in artificial intelligence-based autonomous vehicles
    Medrano-Berumen, Christopher
    Akbas, Mustafa Ilhan
    [J]. JOURNAL OF INFORMATION AND TELECOMMUNICATION, 2021, 5 (01) : 83 - 103