  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.
