LAUSR

This article introduces a new persistent surveillance problem, which is to detect events that randomly occurred on a road network by cooperative unmanned ground vehicles (UGVs) with a certain detection ability, where the road network is simplified into weighted viewpoints and path segments. To tackle the road network cooperative persistent surveillance problem, this article seeks to: 1) evaluate the surveillance effect, and 2) plan the trajectories of UGVs to achieve the optimal surveillance effect. First, the nonconfidence level of event occurrence status at the viewpoint, or the uncertainty, which is derived from the detection ability, the monitoring interval, and the weight of viewpoints, is defined as a performance criterion to evaluate the surveillance effect. Then, the new persistent surveillance problem is formulated as a combinatorial optimization problem and proved to be NP-hard. Considering the characteristics of the problem, a heuristic cooperation path planning algorithm with polynomial time complexity is designed to generate noncyclic trajectories. Theoretical analysis and simulation results demonstrate the feasibility of the proposed algorithm on road networks of any topology. By comparing with the existing methods in simulations and outdoor experiments, it demonstrates that the proposed algorithm has the superiority of reducing the uncertainty of the road network and improving the surveillance efficiency in real-world applications.