Mobile sensor patrol path planning in partially observable border regions

摘要

A border surveillance operation requires sophisticated sensor planning, as sensors are usually scarce and cannot cover an entire region simultaneously. Border patrol agents act as moving sensors in a border region, and the border patrol agents’ coverage moves around the region dynamically, increasing the chance of approaching a trespassing agent. Typically, the locations of trespassing agents cannot be fully observed due to the size of the border region and obstacles. In addition, intelligent trespassing agents may dynamically adjust their traveling paths so that the border patrol agents cannot predict their locations easily. Trespassing agents are assumed to leave traces that indicate their footprints, providing their estimated locations. Border patrol agents may use the trespassers’ footprints as partial information to leverage patrol path planning. We propose an adaptive border patrol process as a partially observable Markov decision process (POMDP), in which an individual border patrol agent’s decision is determined dynamically on the basis of trespassing agents’ partially observed locations. The observations are shared among individual border patrol agents, allowing the border patrol agents to cooperate. The zoning technique is used to limit the planning scope of an individual border patrol agent, and Monte Carlo simulation is applied to reduce the complexity of the POMDP planning problem. Empirical experiments are conducted by means of simulated agents. The simulation parameters are derived from the interviews with a group of border patrol experts. The results in different scenarios show that the proposed patrol path planning scheme outperforms other patrol path planning schemes in terms of the trespasser detection rate. The simulation results are validated with respect to subject matter experts (SMEs), where SMEs are the same border patrol experts who had given the interviews. The proposed method has potential in border surveillance as an assisting system for human border patrol or an automated guidance system in robots or drones.