Adaptive Partitioning for Coordinated Multi-agent Perimeter Defense

Multi-Robot Systems have been recently employed in different applications and have advantages over single-robot systems, such as increased robustness and task performance efficiency. We consider such assemblies specifically in the scenario of perimeter defense, where the task is to defend a circular perimeter by intercepting radially approaching targets. Possible intruders appear randomly at a fixed distance from the perimeter and with azimuthal location determined by some unknown probability density. Coordination among multiple defenders is a complex combinatorial optimization problem. In this work, we focus on the following two aspects: (i) estimating the probability density that describes the direction from which the next intruders are going to arrive, and (ii) partitioning of the space so that the defenders focus on capturing a disjoint subset of intruders. Results show that the proposed strategy increases the number of captures over a naive baseline strategy, especially in scenarios with non-uniform spatial distributions of intruder arrival. The proposed approach is also efficient and able to quickly adapt to time-varying intruder distributions.