Computationally Efficient Model Predictive Control for Multi-Agent Surveillance Systems

抄録

In this paper, a surveillance system by multiple agents, which is called a multi-agent surveillance system, is studied. A surveillance area is given by an undirected connected graph. Then, the optimal control problem for multi-agent surveillance systems (the optimal surveillance problem) is to find trajectories of multiple agents that travel each node as evenly as possible. In our previous work, this problem is reduced to a mixed integer linear programming problem. However, the computation time for solving it exponentially grows with the number of agents. To overcome this technical issue, a new model predictive control method for multi-agent surveillance systems is proposed. First, a procedure of individual optimization, which is a kind of approximate solution methods, is proposed. Next, a method to improve the control performance is proposed. In addition, an event-triggering condition is also proposed. The effectiveness of the proposed method is presented by a numerical example.