One of the challenges of Industry 4.0, is to determine and optimize the flow of data, products and materials in manufacturing companies. To realize these challenges, many solutions have been defined such as the utilization of automated guided vehicles (AGVs). However, being guided is a handicap for these vehicles to fully meet the requirements of Industry 4.0 in terms of adaptability and flexibility: the autonomy of vehicles cannot be reduced to predetermined trajectories. Therefore, it is necessary to develop their autonomy. This will be possible by designing new generations of industrial autonomous vehicles (IAVs), in the form of intelligent and cooperative autonomous mobile robots.In the field of road transport, research is very active to make the car autonomous. Many algorithms, solving problematic traffic situations similar to those that can occur in an industrial environment, can be transposed in the industrial field and therefore for IAVs. The technologies standardized in dedicated bodies (e.g., ETSI TC ITS), such as those concerning the exchange of messages between vehicles to increase their awareness or their ability to cooperate, can also be transposed to the industrial context. The deployment of intelligent autonomous vehicle fleets raises several challenges: acceptability by employees, vehicle location, traffic fluidity, vehicle perception of changing environments (dynamic), vehicle-infrastructure cooperation, or vehicles heterogeneity. In this context, developing the autonomy of IAVs requires a relevant working method. The identification of reusable or adaptable algorithms to the various problems raised by the increase in the autonomy of IAVs is not sufficient, it is also necessary to be able to model, to simulate, to test and to experiment with the proposed solutions. Simulation is essential since it allows both to adapt and to validate the algorithms, but also to design and to prepare the experiments.To improve the autonomy of a fleet, we consider the approach relying on a collective intelligence to make the behaviours of vehicles adaptive. In this chapter, we will focus on a class of problems faced by IAVs related to collision and obstacle avoidance. Among these problems, we are particularly interested when two vehicles need to cross an intersection at the same time, known as a deadlock situation. But also, when obstacles are present in the aisles and need to be avoided by the vehicles safely.

翻译：工业4.0的挑战之一在于确定并优化制造企业中的数据、产品和物料流。为实现这些目标，业界已提出多种解决方案，例如采用自动导引车。然而，导引特性限制了这些车辆在适应性与灵活性方面完全满足工业4.0的要求：车辆的自主性不能局限于预设轨迹。因此，必须提升其自主能力。这需要通过设计新一代工业自主车辆来实现，即智能协同的自主移动机器人。在道路运输领域，实现汽车自主化的研究十分活跃。许多解决交通场景问题的算法（与工业环境中可能出现的场景相似）可迁移至工业领域并应用于工业自主车辆。由专业机构（如ETSI TC ITS）标准化的技术，例如车辆间为提升环境感知或协同能力而进行的信息交换技术，同样可移植到工业场景中。智能自主车队的部署面临多重挑战：员工接受度、车辆定位、交通流畅性、车辆对动态变化环境的感知、车-路协同以及车辆异构性等。在此背景下，提升工业自主车辆的自主性需要采用合理的工作方法。仅识别可复用或可适配的算法来解决工业自主车辆自主性提升带来的各类问题并不足够，还必须能够对提出的解决方案进行建模、仿真、测试与实验验证。仿真至关重要，它既能用于算法适配与验证，也能为实验设计与准备提供支持。为提升车队自主性，我们采用基于群体智能的方法使车辆行为具备自适应性。本章将聚焦于工业自主车辆面临的碰撞与障碍物规避问题类别。其中我们特别关注两类场景：其一是两辆车需同时通过交叉路口导致的死锁情形；其二是通道中存在障碍物时车辆需进行安全规避的情况。