Swarm Approaches for the Patrolling Problem, Information Propagation vs. Pheromone Evaporation Hoang-Nam Chu 1,2 , Arnaud Glad 1 , Olivier Simonin 1 , François Sempé 2 , Alexis Drogoul 3,2 , François Charpillet 1 1 MAIA, INRIA Lorraine, Campus scientifique, BP 239, 54506 Vandœuvre-lès-Nancy, France 2 Institut Francophone pour l’Informatique, Hanoï, Vietnam 3 IRD - Institut de Recherche pour le Développement, Bondy, France {hoangnam.chu, arnaud.glad, olivier.simonin, francois.charpillet}@loria.fr francois@ifi.edu.vn, drogoul@mac.com Abstract 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. Keywords: Multi-agent patrolling, reactive multi- agents system, digital pheromones. 1. Introduction Patrolling consists in deploying a set of agents (robots) in an environment in order to visit regularly all the accessible places [5]. This problem was studied in recent years according to centralized, heuristic and distributed approaches, but always within a discrete representation of the environment, i.e. a graph. A vertex is a predetermined place that should be visited and an edge is a valid path linking two places. Thus, various work based on graph search algorithms have been proposed, often deriving from the problem of the traveling salesman (cf. [1] for a presentation of these various techniques and their comparison). For instance Lauri and Charpillet [4] proposed a solution relying on ACO algorithms (ants colonies optimization) which requires a representation of the environment through a graph. There are also approaches based on learning techniques (e.g. [8]). They consist in computing offline an optimal multi-agent path, which is then carried out online in the considered environment. Consequently, this type of solution is not able to self-adapt to online changes of the problem/environment, such as variations of the number of agents or moves of obstacles, etc. Moreover, these approaches are subject to combinatory explosion when the graph size becomes important (several hundreds of nodes) or when the number of deployed agents increases. However, nowadays, many concrete applications present the patrolling problem on large spaces, known or unknown, with a significant number of agents (drones deployed to supervise a strategic place, patrolling of buildings by mobile robots, etc.). So, to deal with such a configuration of the problem (unknown environments) we think that swarm intelligence could be an efficient approach. It is generally based on the marking of the environment, inspired by the ants’ pheromone drop, which defines an indirect calculation and means of communication among the agents [2]. These digital pheromones rely on two processes calculated by the environment: the diffusion and the evaporation of information (pheromone’s quantity). The diffusion process enables the propagation of the information by effect of vicinity, while evaporation allows removing gradually the information. Sempé et al. [10] proposed in 2003 an algorithm named CLInG exploiting the propagation of information, which is close to the diffusion process, showing the interest of an approach based on an active environment. However this approach appears relatively expensive as it exploits processes of propagation and