Event-Driven Receding Horizon Control for Distributed Persistent Monitoring on Graphs

We consider the optimal multi-agent persistent monitoring problem defined on a set of nodes (targets) inter-connected through a fixed graph topology. The objective is to minimize a measure of mean overall node state uncertainty evaluated over a finite time interval by controlling the motion of a team of agents. Prior work has addressed this problem through on-line parametric controllers and gradient-based methods, often leading to low-performing local optima or through off-line computationally intensive centralized approaches. This paper proposes a computationally efficient event-driven receding horizon control approach providing a distributed on-line gradient-free solution to the persistent monitoring problem. A novel element in the controller, which also makes it parameter-free, is that it self-optimizes the planning horizon over which control actions are sequentially taken in event-driven fashion. Numerical results show significant improvements compared to state of the art distributed on-line parametric control solutions.