Olivier Simonin

Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation

This paper deals with the multi-agent patrolling problem in unknown environment using two collective approaches exploiting environmental dynamics. After specifying criteria of performances, we define a first algorithm based only on the evaporation of a pheromone dropped by reactive agents (EVAP). Then we present the model CLInG [10] proposed in 2003 which introduces the diffusion of the idleness of areas to visit. We systematically compare by simulations the performances of these two models on growing- complexity environments. The analysis is supplemented by a comparison with the theoretical optimum performances, allowing to identify topologies for which methods are the most adapted.

Theoretical Study of Ant-based Algorithms for Multi-Agent Patrolling

This paper addresses the multi-agent patrolling problem, which consists for a set of autonomous agents to visit all the places of an unknown environment as regularly as possible. The proposed approach is based on the ant paradigm. Each agent can only mark and move according to its local perception of the environment. We study EVAW, a pheromone-based variant of the EVAP [3] and VAW [12]. The main novelty of the paper is the proof of some emergent spatial properties of the proposed algorithm. In particular we show that obtained cycles are necessarily of same length, which ensures an efficient spatial distribution of the agents. We also report some experimental results and discuss open questions concerning the proposed algorithm.

Safe longitudinal platoons of vehicles without communication

This paper deals with the platooning problem that can be defined as the automatic following of a manned driven vehicle by a convoy of automatic ones. Different approaches have been proposed so far. Some require the localisation of each vehicle and a communication infrastructure, others called near-to-near approach only needs vehicle on-board sensors. However, to our knowledge, they do not provide any proof of non collision. We propose a novel near-to-near longitudinal platooning building a collision-free platooning whatever the number of vehicles. The model is derived from the study of the most dangerous interaction between two vehicles, i.e. considering the maximum acceptable acceleration when the previous vehicles brakes at maximum capacity. Collision avoidance of this model is proved. Finally, we show that this model can be combined to existing ones, keeping this collision-free property while allowing more various behaviors.

Self-Organization of Patrolling-Ant Algorithms

We consider here multi-agent patrolling as the task for a group of agents to repeatedly visit all the cells of a discrete environment. Wagner et al. have introduced patrolling ant algorithms, where each agent can only mark and move according to its local perception of the environment. Among various results, it has been experimentally observed that for some algorithms the agents often self-organize in stable cycles which are near optimal in terms of visit frequency. This property is particularly interesting as it guarantees the long-term performance of the patrol. The present paper focuses on the convergence behavior of a typical ant-based algorithm, EVAW. The main contribution of this paper is to theoretically prove that the group of agents self-organizes in cycles under certain hypotheses. These hypotheses rely on some implementation details that allow to control the predictability of the system. In addition to these qualitative results on the convergence behavior, we aim at experimentally evaluating its characteristics. This led us to a second contribution: an algorithm that detects steady states. Finally, we propose an improved behavior that dramatically speeds up the self-organization and allows us to experiment on larger problems (both in terms of size and number of agents).

Translating Discrete Multi-Agents Systems into Cellular Automata: Application to Diffusion-Limited Aggregation

This paper deals with the synchronous implementation of situated Multi-Agent Systems (MAS) in order to have no execution bias and to ease their programming on massively parallel computing devices. For this purpose we investigate the translation of discrete MAS into Cellular Automata (CA). Contrarily to the sequential scheduling generally used in MAS simulations, CA are a model for massively parallel computing where the updating of the components is synchronous.

Authority Sharing in a Swarm of UAVs: Simulation and Experiments with Operators

It is emphasized in numerous prospective studies that the development of swarms of Unmanned Aerial Vehicules (UAV) should be important in the next years. However, the design of these new multi-agent systems involves to take up many challenges. In particular, reducing the number of operators requires to define new interfaces in order to interact with such autonomous multirobot systems. We present an approach that allows one operator to control a swarm of UAVs in the context of simulated patrolling and pursuit tasks. Self-organized control relying on digital pheromones, as well as authority sharing based on several operating modes are defined. Experiments with human operators on the simulated system show that the combination of the two approaches is effective.

Cooperative Behaviors for the Self-Regulation of Autonomous Vehicles in Space Sharing Conflicts

In real-world multi-agent systems, as in the context of the automatic transportation of goods, autonomous vehicles can face unexpected events like the failure of a vehicle, the presence of obstacles on the road, etc. Such events can generate first local congestions, and then, if they persist, global phenomena and complex traffic congestions (such as traffic jams). We want to manage space sharing conflicts at the local level, when they appear, to allow a quick (real-time) regulation, i.e., without requiring to re-plan the routes of all involved agents. Our approach relies on reactive coordination between vehicles using simple interactions between neighboring agents, using perceptions and little or no communication. We consider in particular a scenario where two queues of vehicles share a single lane, describing the model of the network as well as the agents, and proposing simple coordination rules that only involve the two vehicles at the front of each queue. We then conduct experiments that allow the analysis and the comparison of the proposed self-regulation rules.

Improving near-to-near lateral control of platoons without communication

This paper considers the platooning problem: we aim to steer a train of vehicles along an unknown path generated by the first vehicle, which is human driven. Among existing approaches, we study a decentralised local approach to avoid robustness issues due to communication failure which are common to centralised approaches. However, decentralised control rises up the problem of lateral deviation which is accumulated along the platoon.

Multi-Robot Patrolling in Wireless Sensor Networks Using Bounded Cycle Coverage

Patrolling is mainly used in situations where the need of repeatedly visiting certain places is critical. In this paper, we consider a deployment of a wireless sensor network (WSN) that cannot be fully meshed because of the distance or obstacles. Several robots are then in charge of getting close enough to the nodes in order to connect to them, and perform a patrol to collect all the data in time. We discuss the problem of multi-robot patrolling within the constrained wireless networking settings. We show that this is fundamentally a problem of vertex coverage with bounded simple cycles (CBSC). We offer a formalization of the CBSC problem and prove it is NP-hard and at least as hard as the Traveling Salesman Problem (TSP). Then, we provide and analyze heuristics relying on clusterings and geometric techniques. The performances of our solutions are assessed in regards to networking parameters, robot energy, but also to random and particular graph models.

Mapping likelihood of encountering humans: Application to path planning in crowded environment

An important challenge for autonomous robots is to navigate efficiently and safely in human populated environments. It requires that the robots perceive human motions and take into account human flows to plan and navigate. In this context we address the problem of modeling human flows from the perception of the robots, by defining a grid of the human motion likelihood over the environment, called flow grid. We define the computation of this grid as a counting based mapping. Then we define a path planning taking into account the risk of encountering humans in opposite direction. We first evaluate the approach in simulation by considering different navigation tasks in a crowded environment. For this purpose, we compare three A∗-based path planning models using different levels of information about human presence. Simulations involving 200 moving persons and 4 collaborative robots allow to evaluate simultaneously the flow mapping and the related path planning efficiency. Finally we experiment the model with a real robot that maps human displacements in its environment.