MPC-Based Surveillance over Graphs by Multiple Agents

Abstract

: The multi-agent surveillance problem over graphs is to find trajectories of multiple agents that travel each node as evenly as possible. This problem has several applications such as city safety management and disaster rescue. In this paper, the finite-time optimal surveillance problem is formulated, and is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, an optimal trajectory is generated by solving the MILP problem at each discrete time, and persistent surveillance can be realized. Finally, a numerical example is presented, and further discussion is also provided.

Optimal Monitoring of Multiple Agents via Time Sequence-Based Modeling

“…Problem 1 can be equivalently rewritten as an MILP problem [6]. In the case where m and N are large, the computation time for solving the MILP problem becomes long.…”

“…The authors have studied in [6] the optimal monitoring problem over a graph. In this method, this problem is rewritten as a mixed integer linear programming (MILP) problem, which can be solved by a suitable commercial/free solver.…”

“…In other words, the adjacency relation of a given graph is time varying depending on the current locations of agents. In [6], the adjacency relation is time invariant, and the unnecessary candidates of the next locations are included. This is the difference between the proposed method and the ex-isting method [6].…”

“…In [6], the adjacency relation is time invariant, and the unnecessary candidates of the next locations are included. This is the difference between the proposed method and the ex-isting method [6]. By eliminating the unnecessary candidates, the computation time for solving the optimal monitoring problem is improved.…”

“…By eliminating the unnecessary candidates, the computation time for solving the optimal monitoring problem is improved. Furthermore, in a similar way to the method in [6], the optimal monitoring problem can be rewritten as an MILP problem. The effectiveness of the proposed method is demonstrated by numerical examples.…”