Pheromone-Diffusion-based Conscientious Reactive Path Planning for Road Network Persistent Surveillance

Abstract

Road Network Persistent Surveillance Problem (RPSP) involves path planning for an unmanned ground vehicle (UGV) with detection ability to timely detect the events randomly occurred. The road network is formed by edges and weighted viewpoints, where the UGV must move along the edges. The existing method based on cognitive architecture is inadequate in terms of real-time and effective decision making. To improve the computation efficiency and accuracy of solving RPSP, a new algorithm called Pheromone-Diffusion-based Conscientious Reactive Persistent Surveillance (PD-CRPS) is proposed in this paper. Considering the detection ability of UGV, the monitoring weight, the surveillance effect, and the topology of the road network, a model of pheromone release and diffusion is established to estimate the global uncertainty through the local information around the UGV. The local optimum avoidance technology based on pheromone is designed, and the reactive architecture is used to design the payoff function of decision making. The worst-case computational time complexity of the PD-CRPS is far less than the existing cognitive architecture method. Simulation results show that the PD-CRPS can not only efficiently plan the path of UGV in the road networks with different topologies and observation obstacles, but also improve the calculation accuracy.