Modeling and Path Planning for Persistent Surveillance by Unmanned Ground Vehicle

This article proposes a novel modeling and path planning framework to tackle a new problem, named road-network persistent surveillance problem (RPSP), in which the occurrence location and probability of the events are unknown. To capture such events, an unmanned ground vehicle (UGV) with certain detection ability must move along the road-network with different monitoring priorities to persistently monitor them. First, we presented an event-oriented modeling method to formulate the new problem. The monitoring effect of the road-network is quantified by uncertainty, which takes the detection ability of the sensor, the detection interval, and the monitoring weight into account. Based on the proposed model, we then designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road-network without traversing them. The simulation results and analysis demonstrate the feasibility and superiority of the proposed approach. Note to Practitioners-This article was motivated by developing an effective solution for unmanned ground vehicle (UGV) to execute road-network persistent surveillance tasks under outdoor circumstances. To the best of our knowledge, there is no other consideration dealing with this problem. To capture spatiotemporal random events, we proposed a new approach that enables UGV to persistently monitor an area with road-network performing in the weighted mode. Distinguished from the traditional path planning methods in the literature, the proposed approach considered the detection ability of the UGV. Consequently, the so-called 'area-To-Area' planning approach does not require traversing all the viewpoints. The approach can be used in road-network with any topology. Moreover, the difficulty in modeling road-network from the actual environment is degraded, and compared with the existing patrolling methods, the monitoring efficiency of the proposed approach is improved obviously.