Persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. UAVs can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this paper, we consider the case in which the service station is not co-located with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly, when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.
翻译:持续监测任务需要随时了解基本环境的变化情况。无人驾驶航空器可以被有收益地用于持续访问代表环境任务(和地点)的一组目标,并长期收集其中的数据。这些任务具有持久性,需要定期在服务站对无人驾驶航空器进行补给。在本文中,我们考虑了服务站与任何目标不在同一地点的情况。高效监测需要重访时间,即从连续重访到目标之间的最长时间,以尽量缩小。在这里,我们考虑确定无人驾驶航空器通往最低重访时间的路线的问题。问题在于NP硬性,其计算困难随着无人驾驶飞行器的燃料能力增加。我们开发了一种算法,以便在燃料能力超过临界值时迅速构建近乎最佳的解决问题的办法。我们还开发了与最佳重访时间的较低界限,并使用这些界限来证明(通过数字模拟)所建造的解决方案平均最多为0.01%,离最佳的距离是0.01%。