J Grosset

A.-J Fougères

M Djoko-Kouam

J.-M Bonnin

In-person: ☐ Virtual in Zoom: ☐ Collective obstacle avoidance strategy -an agent-based simulation approach

Keywords: Applied Artificial Intelligence Agent modelling, cooperative strategy, simulation, industry 4.0, autonomous industrial vehicles, obstacle avoidance

The context of factory 4.0 leads more and more to decentralised solutions, as centralisation shows its limits. One of the research areas of Industry 4.0 is the use of autonomous guided vehicles (AGVs), autonomous industrial vehicles (AIVs). We want to show that cooperation is useful and necessary to increase their autonomy. We propose in this paper an agent model to test scenarios in Industry 4.0 environments with a fleet of AIVs. In addition, we are interested in the resolution of global obstacle avoidance by AIVs with a collective strategy. The results of the simulation will be evaluated by performance indicators such as distance and time in order to compare the different proposed approaches.

Introduction

One of the fundamental axes of Industry 4.0 is the search for decentralised solutions in the decisionmaking process because centralisation quickly shows its limits [START_REF] Boccella | Evaluating Centralized and Heterarchical Control of Smart Manufacturing Systems in the Era of Industry 4.0[END_REF]. Indeed, if an industry is centrally controlled, then if a technical failure, e.g., a network failure occurs, everything will come to a standstill. Decentralised solutions allow for more flexible control, which is preferable.

The use of autonomous guided vehicles (AGVs) or autonomous industrial vehicles (AIVs) is a strategic research area in the context of Factory 4.0 and Logistic 4.0. Currently, vehicles have autonomy over their navigation linked to rails, physical or virtual markers that allow them to get to their goal. They follow predetermined trajectories and they are autonomous in their navigation after having received an order from a central, a supervisor. If they detect a problem, or an obstacle they can report the information to the central, but it is the control centre that will decide for the other vehicles.

Our point of view to improve the autonomy of AIVs is to move towards decentralised solutions with collective strategies taken by the fleet of vehicles. To increase their intelligence and their individual and collective decision-making, they need to become more cooperative [START_REF] Cao | Cooperative Mobile Robotics: Antecedents and Directions[END_REF][START_REF] Ismail | A survey and analysis of cooperative multi-agent robot systems: challenges and directions[END_REF][START_REF] Rizk | Cooperative heterogeneous multi-robot systems: A survey[END_REF]. Thus, the AIVs will be more efficient in performing the five core tasks they have to control (task allocation, localisation, path planning, motion planning and vehicle management) [START_REF] De Ryck | Resource management in decentralised industrial automated guided vehicle systems[END_REF].

Thus, they could carry out their missions with a collective strategy. This translates for example into a fleet of AIVs that communicate and share information. In the first level of strategy, there is the shared perception of their vision of the environment. A higher level that would achieve a collective global strategy for the realisation of missions would be the allocation, scheduling and distribution of tasks between them in real-time. The aim is for robots to be more efficient, waste less time and use less energy to accomplish all their tasks.

Our objective is to improve the AIV autonomy integrated into a fleet based on collective intelligent strategies. Among the problems to be solved to make AIVs more autonomous, we can particularly identify 1) the avoidance of collisions between vehicles or with static or dynamic obstacles [START_REF] Haddadin | Robot collisions: A survey on detection, isolation, and identification[END_REF][START_REF] Nascimento | A systematic literature review about the impact of artificial intelligence on autonomous vehicle safety[END_REF], and 2) the path planning or path finding when an obstacle hinders the passage of AIVs or obstructs a predefined path [START_REF] Kunchev | Path planning and obstacle avoidance for autonomous mobile robots: a review. 10th international[END_REF][START_REF] Ma | Optimal target assignment and path finding for teams of agents[END_REF][START_REF] Kim | A guide to selecting path planning algorithm for automated guided vehicle (AGV)[END_REF].

The problem of avoidance is an individual problem that is addressed in a more global and collective way. There are three phases: perception of the obstacle (detection), rerouting or trajectory planification (avoidance) and the overall strategy. In the following, we will deal with avoidance through the choice of the route, the path but not the trajectory. This chosen path can be costly when an aisle is completely obstructed. This can be solved by enhanced cooperation [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF][START_REF] Hafner | Cooperative collision avoidance at intersections: Algorithms and experiments[END_REF], i.e., the ability of autonomous industrial vehicles (AIVs) to exchange relevant information with each other on their traffic conditions. This is how we augmented the Bahnes' algorithm [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF], in a previous work [START_REF] Grosset | Simulation of a collision and obstacle avoidance algorithm for cooperative industrial autonomous vehicles[END_REF], to take into account obstacle avoidance, in addition to communication for avoidance at intersections.

Solutions are needed for the second problem, especially in the case where an obstacle completely obstructs a lane. This prevents a mission from not being completed because the main route defined by an AIV is compromised by an obstruction. Path planning or path-finding algorithms [START_REF] Ma | Optimal target assignment and path finding for teams of agents[END_REF][START_REF] Karur | A Survey of Path Planning Algorithms for Mobile Robots[END_REF] make it possible to re-plan their route, depending on whether the target of the mission has been reached or not.

In this paper, we will propose a global obstacle avoidance with shared perception. Indeed, we will not use a local avoidance solution to improve autonomy. But rather a collaboration between the different vehicles of the fleet to move towards the basis of a global collective strategy in our future work. The proposed approach is adaptable and flexible for centralised or decentralised solutions. In other words, we are dealing with a distributed system where each robot decide by itself, augmented with communication between vehicles to share information about the environment. The communication may also go through a central supervisor before being redistributed.

The paper is organised as follows: section 2 describes the implementation of a collective strategy consequent to the detection with agent modelling, the exchange of messages to cooperate and the different approaches we will compare. The description of the simulation environment, the implementation of the experiments and the results are analysed in section 3. A discussion on the cooperative infrastructure and how fuzzy logic can help to qualify the obstacles are also made in section 3. Finally, conclusions and future work are presented in section 8.

Collective strategy consequent to the detection

Modelling agent

In order to validate the use of collective strategy algorithms to increase the autonomy of AIVs, we rely on a methodological framework consisting of 3 phases:

(1) agent modelling, (2) verify in simulations the correct performance of the algorithm, and (3) experiment with TurtleBot3 Burger robots in perspective.

We propose an agent model in Fig. 1 that adapts to different situations and scenarios that are tested in the context of Industry 4.0. Infrastructure is deployed in the environment and is composed of a traffic plan, and active elements such as beacons, tags and stations. Since they are active, these elements of infrastructure are modelled as software agents. These agents are defined as fuzzy agents because they can take fuzzy decisions from uncertain knowledge or perception, but these possibilities are not illustrated in this paper.

Industrial vehicles, which are also modelled as autonomous agents, carry out missions defined by routes and sent by a supervisor of the environment. They are equipped with radar and thanks to their knowledge of the environment they can avoid collisions with static obstacles (goods in the aisles), or dynamic obstacles (other vehicles or operators). AIVs can also cooperate by communicating together. We choose to use different types of standardised messages transposed from ETSI for ITS.

Exchanging messages to cooperate

The solutions proposed in the literature make it possible to solve an avoidance problem locally. In order to be able to carry out all the missions assigned to the vehicles, it is necessary that they cooperate and share information on their perception of the environment. Indeed, if an obstacle is perceived by a vehicle, the sending of a Cooperation Perception Message (CPM) (ETSI TR 103 562 standard [START_REF]Intelligent Transport Systems (ITS). Vehicular Communications; Basic set of Applications; Analysis of the Collective Perception Service (CPS), Release 2[END_REF]) makes it possible to warn and help the other vehicles to make decisions about their route. This means that if an unavoidable obstacle is perceived on its route, it can recalculate and plan a new route to be able to complete its task.

Obstacle avoidance approaches

We propose to study three approaches to obstacle avoidance by AIVs that will be tested in the simulation. Each robot has knowledge of the environment, i.e., the position of aisles, intersections, AIVs stations, and mission points. These various important traffic points of the circuit will be called nodes in the following, and are identified by respective numbers shown in Fig. 3.

In the first approach, agents do not have the ability to re-plan a route and change their route when faced with an obstacle.

In the second approach, vehicles are able to change routes and plan a new route when an obstacle is detected, but they do not cooperate with other vehicles. This means that vehicles will enter routes where obstacles are present and will have to change their route, even though another vehicle has already perceived this. They are able to calculate the path to their destination.

The path planning algorithm chosen for these experiments is Dijkstra. The objective is not to optimise a path planning algorithm (each AIV agent having to execute this algorithm), but to use an efficient algorithm to measure the performance of a collective strategy compared to an individual approach in carrying out missions in an industrial environment.

The last approach will be a collective strategy upon detection of an obstacle. This strategy is established according to two points of view:

-The vehicle detects an obstacle, which determines the level of obstruction. Then, it communicates the description (position, level of obstruction, type -static or dynamic) of the obstacle to the other vehicles. It can react according to its static or dynamic type to avoid it if possible, or it can replan another path to reach its objective and accomplish its mission. This algorithm is described in Fig. 2: algorithm 1. -The vehicle receives information about an obstacle in the environment. It first tries to determine whether the obstacle is on its path to accomplishing its mission. If this is the case and the obstacle is obstructing its planned path, it can replan its route. If the obstacle can be avoided, it can act and anticipate this static or dynamic obstacle by arriving in the vicinity of the received position of the obstacle. This improves the safety of the area and prevents accidents. This algorithm is described in Fig. 3: algorithm 2.

Simulation results and discussion

Environment simulation

The environment chosen to test the different approaches is the warehouse presented by Bechtsis et al. [START_REF] Bechtsis | Intelligent autonomous vehicles in digital supply chains: a framework for integrating innovations towards sustainable value networks[END_REF]. The directions of circulation have been adapted for our experiments and are identified in Fig. 3. We work with five AIVs corresponding to the five parking spaces available in the environment.

AIVs must perform missions in the environment shown in Fig. 3. A mission consists of travelling from its car park to a storage point and back to its car park. Thus, the AIVs applying the collective intelligent strategy have two route calculations to perform with the Dijkstra algorithm. The first one is to calculate the shortest path to the storage point. The second one is to calculate the shortest route back to their assigned parking space. The costs in distance between the different nodes of the circuit have been chosen and applied to favour certain directions of traffic. For example, vehicles should go from node 14 to node 6, which has a cost of 10, rather than to node 5, which has a cost of 40. These costs between the different nodes are shown in the directed graph in Fig. 3. These costs are used by Dijkstra's algorithm to find the path that costs the least distance, and therefore the fastest.

To simulate these different experiments, we implemented a graphical application with different functionalities. The different classes presented in Fig. 1 have been implemented in Python from scratch. We use and develop this simulator for various experiments in the laboratory, and for teaching to engineering students. Its interface is shown in Fig. 4. In the central part, we have the representation of the warehouse presented by Bechtsis et al. [START_REF] Bechtsis | Intelligent autonomous vehicles in digital supply chains: a framework for integrating innovations towards sustainable value networks[END_REF], which we have adapted in Fig. 3. The different nodes present in Fig. 3 and Fig. 4 are represented by white squares in the interface. The vehicles are visualised by small coloured circles, and the obstacles are coloured black with different sizes corresponding to the obstruction levels in the aisle.

In the following, we will denominate the scenarios sc1, sc2 and sc3. The missions will be named mi and the nodes ni.

The functionalities proposed by our application (Fig. 4) are to:

-Stop the simulation.

-Place obstacles randomly in terms of size T1 to T4 (T4 corresponding to a size that obstructs an entire aisle -see Fig. 4) and position at the six possible nodes shown in yellow in Fig. 3. -Launch a scenario: the agents must then carry out missions given by the supervisor. That is, go from their parking place to one of the three green nodes in Fig. 3, before returning to their parking place. On the other hand, pressing the scenario 1, 2 or 3 button allows them to choose which strategy (sc1, sc2 or sc3) they can perform. For instance, in Fig. 4, an avoidable obstacle with a size T2 in n6 is present, and an obstacle obstructs the whole passage with a size t4 in n8. The size of the obstacle in the interface corresponds to its obstruction level.

These different approaches were detailed in subsection 3.3. Sc1, therefore, does not allow the agents to use Dijkstra's algorithm. Sc2 does, and sc3 allows agents to communicate with each other about the description of a perceived obstacle.

Setting up the experiments

The three approaches were tested in six different experiments. Each experiment corresponded to an obstacle completely obstructing an aisle on one of the six nodes: n4, n5, n6, n7, n8 and n9 shown in orange in Fig. 3. During these six experiments, the agents had to perform 1 of 3 missions consisting in going from the car park to one of the nodes n13, n14 and n15 randomly chosen by the supervisor, and then returning to their parking place. They were equipped with one of the three approach capabilities described in subsection 3.3. That is, three experiments were performed with sc1, sc2 and sc3 with an obstruction at the same node ni to compare the approaches. The performance indicators that were used to define the efficiency and performance of these approaches are:

-The validity of the set of missions performed by the five agents. -The distance travelled to carry out the different missions. -The overall time is taken to complete the various tasks. Distance and time are performance indicators that were chosen because they are strongly related to the energy used by a robot, an AIV. Thus, this indicates the energy impact of the different approaches.

Experiments: comparisons with performance indicators

Table 1 summarises the different results of the agents in the six experiments according to sc1, sc2 or sc3. Sc1 does not allow the agents to complete all the missions, which is explained by the fact that they are not able to recalculate their routes. Thus, if any agent was blocked by an obstruction on a node, it remained without the ability to react and the agents' overall mission failed.

Conversely, sc2 and sc3 applied by the agents allowed in both cases to fulfil all the missions thanks to their ability to adapt and to plan a new route (with the Dijkstra algorithm) when encountering an obstacle obstructing a path.

The distance and overall execution time to complete all the missions have therefore been quantified in Table 2 and Table 3 respectively.

The analysis that emerges is that sc3 allows the agents to travel less distance in 4 out of 6 experiments and allows them to complete the missions more quickly in all six experiments.

The cooperative perception allows the agents to not go directly to the obstacle if it is on their way, but to redefine with the help of Dijkstra another path to reach their goal. Thus, logically, this collaboration takes precedence over the individual strategy, and communication in a fleet of AIVs thus increases their autonomy and their ability to adapt to new situations. Over the six experiments, there is a 3% gain in overall distance and a 9% gain in time for the whole fleet of AIVs. This cooperative perception is an essential basis for a future collective global strategy for the sharing and organisation of distributed missions within a fleet.

Cooperative infrastructure

After, the different results mentioned in the previous section, we will discuss the possible cooperation of the infrastructure. Indeed, it could help the collective AIVs strategy with local information. This knowledge of the environment could be shared by active elements of the infrastructure such as cameras. This could help to qualify an obstacle or even detect an obstacle before a vehicle enters an area with an unavoidable obstacle.

Fuzzy logic to qualify obstacles

In perspective, the AIV agents of our model presented in Fig. 1 may become fuzzy. Indeed, the agents will have incomplete, fragmented, fuzzy and uncertain knowledge in certain situations. The fuzzy logic could allow the agents to be able to recalculate or not their itinerary according to whether an obstacle seems avoidable or not, depending on the qualification of the disturbance caused by the obstacle.

Conclusions and perspectives

In the context of Industry 4.0, mobile robots must become increasingly autonomous in order to avoid obstacles in an intelligent way. We are interested in the possible impact of cooperative perception between the vehicles of a fleet during a mission. Our aim is to move to a higher level of collaboration with the possibility of mission sharing and reorganisation in a distributed way.

The comparison of the three approaches evaluated using distance, execution time and whether or not a mission has been successfully achieved, showed that it is essential that vehicles have the ability to re-plan their routes while necessary. In addition, the last approach, which allowed agents to communicate the description of perceived obstacles, allowed for gains in distance and execution time of overall missions. Indeed, it allows AIVs to avoid traffic around an area where a static or dynamic obstacle (human operator, for instance) is present in an aisle. It also allows operators or other robots to be safe in that area: for example, to remove a static obstacle.

This very preliminary study already shows the interest of collaboration to increase the collective and individual efficiency of the vehicles in a fleet. It opens the door to more advanced global collective strategies with the possibility of the allocation, scheduling and distribution of tasks between them in real-time after the perception of an obstacle. Furthermore, the tracking of obstacles by the robots, or assisted by the infrastructure, i.e. a cooperative perception, would also optimise the efficiency of the fleet in carrying out these missions.

The perspectives that emerge from this work are to increase the work of the analysis with other performance indicators, but especially more experiments. Indeed, we can define several missions in a row by robots with several obstacles obstructing the lanes at different places. Another strong perspective to our work is to experiment the collective strategy with the TurtleBot3 Burger robots.

In addition, the notions of cooperative infrastructure and the use of fuzzy logic to qualify obstacles are also planned to be further developed in future work.

Fig. 1 .

 1 Fig. 1. Modelling agents adaptable to different scenarios for Industry 4.0

Fig. 2 .

 2 Fig. 2. Collective intelligent strategy algorithm

Fig. 3 .

 3 Fig. 3. Warehouse environment [16] with a) adapted direction of traffic, and b) it's oriented graph with costs.

Fig. 4 .

 4 Fig. 4. Application interface to simulate missions in the warehouse environment

Table 1 .

 1 Completed missions in relation to the strategy number of agents

Table 2 .

 2 Total distance travelled by agents in relation to the strategy number

Table 3 .

 3 Total execution time to carry out the missions in relation to the strategy number

th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2022), 19-21 October 2022, Corfu, Greece

Acknowledgements

The authors would like to thank the region Bretagne-France for funding the VIASIC project as part of the ARED-2021-2024 call for projects concerning the strategic innovation area: Economics for industry for intelligent production.


J Grosset

A.-J Fougères

M Djoko-Kouam

C Couturier

J.-M Bonnin

Simulation of a collision and obstacle avoidance algorithm for cooperative industrial autonomous vehicles

Keywords: Simulation, autonomous vehicles, cooperative collision avoidance

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 intelligent autonomous vehicles (IAVs) 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. The implantation and deployment of IAVs fleets raises several challenges: acceptability by employees, vehicle location, traffic fluidity, vehicle perception of changing environments. Simulation offers a good framework for studying solutions for these different challenges. Thus, we propose in this paper the extension of a collision detection algorithm to deal with the obstacle avoidance issue. The conclusive simulation will allow us to experiment in real conditions.

Introduction

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 [START_REF] Andreasson | Autonomous transport vehicles: Where we are and what is missing[END_REF] such as the utilization of automated guided vehicles (AGVs), intelligent autonomous vehicles (IAVs) and other cooperative autonomous robots. The implantation and deployment of intelligent autonomous vehicle fleets raises several challenges: acceptability by employees, vehicle location, traffic fluidity, vehicle perception of changing environments (dynamic). In this context, autonomy is reduced to predetermined trajectories.

To improve the autonomy of a fleet, one way it to develop a collective intelligence to make the behaviors of vehicles adaptative. We will focus on a class of problems faced by IAVs related to collision and obstacle avoidance. This occurs 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.

In this paper, we will propose an enhancement to the collision avoidance algorithm experimented in the study [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF], in order to handle the problem of obstacle avoidance.

State of the Art

For the consumer sector, autonomy of vehicles is well determined with 6 levels of autonomy [START_REF] Khayyam | Artificial Intelligence and Internet of Things for autonomous vehicles[END_REF]. However, no such scale exists in the industrial context. Our objective is to improve the IAV autonomy integrated in a fleet based on collective intelligent strategies. Among the problems to be solved to make IAVs more autonomous, we can particularly note the location and positioning of vehicles [START_REF] Ndao | Matrix Beaconing for the Location of Autonomous Industrial Vehicles on a Simulation Platform[END_REF], as well as the avoidance of other vehicles or obstacles [START_REF] Nascimento | A systematic literature review about the impact of artificial intelligence on autonomous vehicle safety[END_REF]. This last problem can be solved by the cooperation between IAVs [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF][START_REF] Hafner | Cooperative collision avoidance at intersections: Algorithms and experiments[END_REF].

The capacity to exchange information between the differents IAVs of a fleet should improve this autonomy [START_REF] Medrano-Berumen | Validation of decision-making in artificial intelligence-based autonomous vehicles[END_REF][START_REF] Daniel | Procuring Cooperative Intelligence in Autonomous Vehicles for Object Detection through Data Fusion Approach[END_REF]. The study [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF] proposed a cooperation strategy based on the exchange of messages to determine the priority to pass an intersection between IAVs. The solution requires the vehicle to know its own position, and to be able to communicate with the other vehicles.

The collision avoidance algorithm presented in [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF] allows IAVs to communicate and cooperate using different types of messages.

The communication between IAVs is done with 3 different types of messages:

• Hello_msg: message to indicate its presence with its position

• Coop_msg: message before an intersection area to determine priority.

• Ack_msg: message to confirm receipt of a Coop_msg.

The European Institute of Telecommunications Standards (ETSI) has published a standard for this kind of cooperative awareness messages (CAM) (ETSI EN 302 637-2 standard [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; Basic set of Applications; Part 2: Specification of Cooperative Awareness Basic Service[END_REF][START_REF] Lyamin | Cooperative awareness in VANETs: On ETSI EN 302 637-2 performance[END_REF]) and decentralized environmental notification message (DENM) (ETSI EN 302 637-3 standard [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; Basic set of Applications; Part 3: Specification of Decentralized Environmental Notification Basic Service[END_REF]). These specifications and messages are approved and constitute building blocks for the safety of future intelligent transport systems (ITS) [START_REF] Santa | Experimental evaluation of CAM and DENM messaging services in vehicular communications[END_REF]. The purpose of CAM messages is similar to Hello_msg in [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF]. They make it possible to locate vehicles in real time relative to each other. DENM messages are alert messages. They are issued at the time of an unexpected event in order to cooperate, warn and disseminate information in the geographical area concerned. They are important messages that would complete the range of possible messages to be exchanged to cooperate and avoid collisions for the IAVs in the Bahnes et al. algorithm [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF].

Algorithm improvement

The collision avoidance algorithm of [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF] makes it possible to deal with the priority of different vehicles when approaching an intersection. However, it does not deal with the problems of detection, communication and avoidance of fixed or moving obstacles (e.g. human operators).

We extend the algorithm of Bahnes et al. to handle the presence of fixed or moving obstacles in Fig. 1.

Then, we simulate the algorithm staying within the framework of the three scenarios proposed by [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF]. These rely on an agent-based model where IAVs are agentified [START_REF] Fougères | A Modelling Approach Based on Fuzzy Agents[END_REF][START_REF]Everything you need to know about agent-based modelling and simulation[END_REF]. Indeed, agent-based simulation for IAVs is the most common in the same way as simulations based on discrete events or robotics software [START_REF] Bechtsis | Intelligent autonomous vehicles in digital supply chains: a framework for integrating innovations towards sustainable value networks[END_REF].

IAV agents have the ability to exchange messages and are equipped with radar. This allows them to detect vehicles in front of them. For instance, given an IAV agent ai, if an other IAV agent aj in front of it is stopped or travelling at a slower speed, the IAV agent ai can detect it with its radar and stop accordingly to avoid hitting it, as in Fig. 2.

To improve the collective autonomy of the IAVs it is essential that they have a good capacity for individual autonomy. The individual autonomy of the IAVs strengthens their collective autonomy.

The messages exchanged by the different IAVs remain consistent with the Bahnes's algorithm. We just propose two new message types for the collaborative perception (added to the three messages defined by Bahnes et al.: Hello_Msg, Coop_Msg and Ack_Msg):

• Obstacle_msg: a message sent by an IAV agent to the other IAV agents circulating in the warehouse to indicate the presence of a perceived obstacle.

• Alert_msg: a message sent by an IAV agent to the other IAV agents circulating in the warehouse to indicate an unavoidable obstacle.

Simulation Results

The traffic plan presented in Fig. 3. shows the different scenarios that we consider as a benchmark plan to compare results. Ten IAVs are distributed over 3 circuits: the red IAVs on the first circuit, the blue ones on the second and the yellow ones on the third.

It involves different intersections, where vehicles can arrive from different sides like in a warehouse (4 intersections are shown in Fig. 3.). Thus, it provides the different characteristics of an industrial environment and allows us to realize simulated experimental tests in line with realistic scenarios.

We notice in the simulation that the avoidance is well respected and the obstacles are perceived by the IAVs. Therefore, the simulation validates the extended Bahnes's algorithm with collision avoidance and fixed or dynamic obstacle detection processing.

Conclusions and Perspectives

In an Industry 4.0 context, many actors cross paths in different areas of a warehouse: vehicles, operators, obstacles (objets that fall or are left in aisles may appear).

The algorithm in [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF] proposed a message-based communication protocol between vehicles to prioritise passage through an intersection. We extended it in order to have the possibility to handle the detection of these fixed and mobile obstacles (Fig. 1).

We have validated the algorithm by a simulation approach with the traffic plan presented in [START_REF] Bahnes | Cooperation Intelligent Autonomous Vehicles to enhance container terminal operations[END_REF].

As an extension of the work and the aim to propose a cooperation protocol for a collaborative collision and obstacle detection, the messages will respect the standards defined by ETSI [START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; Basic set of Applications; Part 2: Specification of Cooperative Awareness Basic Service[END_REF][START_REF]Intelligent Transport Systems (ITS); Vehicular Communications; Basic set of Applications; Part 3: Specification of Decentralized Environmental Notification Basic Service[END_REF].

In future work, we will work to simulate other levels of autonomy using collective strategiescooperation between IAVs but also cooperation integrating the infrastructure and the environment of the IAVs. We also plan to do real experimentations with robots.

Fig. 1 .Fig. 2 .Fig. 3 .

 123 Fig. 1. Improvement of Bahnes's algorithm to treat the problem of collision and obstacles

nd IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0 (ARCI' 2022), 2-4 February 2022, Andorra la Vella, Andorra