Dynamic Interest Points: A Formalism to Identify Areas to Patrol within a Continuous Environment

The multi-agent patrolling problem consists of positioning agents to minimize the idleness, which represents the time difference between two visits of a same location by at least one agent.In the literature, these locations are defined manually by setting static nodes within a graph representation. However, in the context of patrolling a continuous environment, using static nodes cannot guarantee the coverage of the whole environment. In this article, we propose to discretize the continuous environment in order to generate dynamic waypoints called interest points (IP). We prove that these dynamic IP guarantee the coverage of the whole environment while dealing with its topography and the agent's observation range. We evaluated and compared our approach by benchmarking patrolling environment dealing with different observation ranges. Experiments show that dynamic IP locations are adaptive and more efficient to locate high idleness areas compared to static IP approach

I-CMOMMT: A multiagent approach for patrolling and observation of mobile targets with a continuous environment representation

International audienceAgent-based modelling has been widely studied for ob-serving moving targets and patrolling. However, in general, the studies are interested either in observation in a continuous environment or patrolling in a graph representation. In order to deal jointly with observation and patrolling, a common representation of the environment is required. In this paper, we firstly proposed a new environment representation’s formalism, merging both agent-based distributed patrol and observation method. Secondly, we implemented a new approach called I-CMOMMT to cope with a trade-off between observation and patrolling using our new formalism. The obtained results are compared with other methods to show the efficiency of our approach

A decision-making architecture for observation and patrolling problems using machine learning

International audienceObservation and patrolling methods assure the coverage of the entire environment while dealing with moving targets. The efficiency of these methods rely on a wide range of parameters, such as the number of targets, the communication range of the patrolling agent or the map's shape. Thus, in this paper we propose a decision-making tool to optimize a set of parameters among the settings defining the observation and patrolling problem. The obtained optimal configuration has to ensure the expected efficiencies by the user, through the use of evaluation criteria. This tool is based on a simulation-assisted machine learning architecture, which performs a faster prediction response than running the simulation directly to obtain evaluation result. We evaluate the efficiency of the decision-making tool through several scenario, implying one or two parameters to be optimized