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A cooperative approach to avoiding obstacles and collisions between autonomous industrial vehicles in a simulation platform
Integrated Computer-Aided Engineering ( IF 6.5 ) Pub Date : 2022-10-24 , DOI: 10.3233/ica-220694
J. Grosset 1, 2 , A. Ndao 1 , A.-J. Fougeres 1 , M. Djoko-Kouam 1, 3 , C. Couturier 2 , J.-M. Bonnin 2
Affiliation  

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.

中文翻译:

一种在仿真平台中避免自动工业车辆之间的障碍物和碰撞的合作方法

工业 4.0 导致工业流程的强大数字化,但也显着增加了组成它的机器之间的沟通与合作。自动工业车辆 (AIV) 和其他协作移动机器人就是这种情况,它们在工厂中成倍增加,通常以车队的形式,其智能和自主性正在增加。虽然自动驾驶汽车的自主性在道路和公路运输领域得到了很好的表征,但工业中使用的自动驾驶汽车却并非如此。AIV 车队的建立和部署提出了几个挑战,所有这些挑战都取决于 AIV 的实际自主水平:员工的接受程度、车辆位置、交通流动性、碰撞检测或车辆对不断变化的环境的感知。因此,模拟用于说明 AIV 的制造商和未来用户制定的约束和要求。在本文中,在提出了在实际条件下进行实验之前模​​拟 AIV 所要解决的问题的广泛现有技术之后,我们提出了一种估计在封闭工业环境中移动的 AIV 位置的方法,扩展一种碰撞检测算法来处理避障问题,并开发了一个基于代理的仿真平台来模拟这两种方法和算法。结果/最终/后续模拟将使我们能够在真实条件下进行实验。在提出要解决的问题以模拟 AIV 之后,在实际条件下进行实验之后,我们提出了一种估计在封闭工业环境中移动的 AIV 位置的方法,碰撞检测的扩展算法来处理避障问题,并开发了一个基于代理的仿真平台来模拟这两种方法和算法。结果/最终/后续模拟将使我们能够在真实条件下进行实验。在提出要解决的问题以模拟 AIV 之后,在实际条件下进行实验之后,我们提出了一种估计在封闭工业环境中移动的 AIV 位置的方法,碰撞检测的扩展算法来处理避障问题,并开发了一个基于代理的仿真平台来模拟这两种方法和算法。结果/最终/后续模拟将使我们能够在真实条件下进行实验。以及开发基于代理的仿真平台来模拟这两种方法和算法。结果/最终/后续模拟将使我们能够在真实条件下进行实验。以及开发基于代理的仿真平台来模拟这两种方法和算法。结果/最终/后续模拟将使我们能够在真实条件下进行实验。
更新日期:2022-10-24
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