Randomized Multi-Robot Patrolling with Unidirectional Visibility

Patrolling an adversarial environment with multiple robots equipped with vision sensors poses challenges, such as the potential for wireless communication jamming and limited visibility ranges. Early methods relying on deterministic paths are susceptible to predictability by adversaries. Conversely, recent non-deterministic approaches work in discrete environments but overlook sensor footprints and require synchronization. Therefore, this paper proposes an approach to compute patrolling policies for multiple distributed robots that monitor any polygonal environment leveraging limited unidirectional visibility regions in a continuous space and randomized patrolling paths. A visibility roadmap graph is initially constructed from a given environment through its recursive decomposition to account for unidirectional visibility. Our proposed multi-robot task allocation method then partitions the constructed visibility roadmap graph into a set of disjoint subgraphs (areas) and allocates them to multiple robots. Distributed randomized patrolling policies are finally computed in the form of Markov chains, utilizing convex optimization to minimize the average expected commute times for all pairs of locations in allocated areas. We present multiple simulation results to demonstrate the effectiveness of our visibility-based randomized patrolling approach. We also analyze the performance of our approach in detecting targets by robots through a series of simulation runs while they follow the computed policies during patrolling.