College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.
College of Business Administration, Hunan University of Finance and Economics, Changsha 410205, China.
Sensors (Basel). 2022 Apr 10;22(8):2909. doi: 10.3390/s22082909.
With the development of drone technology, drones have been deployed in civilian and military fields for target surveillance. As the endurance of drones is limited, large-scale target surveillance missions encounter some challenges. Based on this motivation, we proposed a new target surveillance mode via the cooperation of a truck and multiple drones, which enlarges the range of surveillance. This new mode aims to rationally plan the routes of trucks and drones and minimize the total cost. In this mode, the truck, which carries multiple drones, departs from its base, launches small drones along the way, surveils multiple targets, recycles all drones and returns to the base. When a drone is launched from the truck, it surveils multiple targets and flies back to the truck for recycling, and the energy consumption model of the drone is taken into account. To assist the new problem-solving, we developed a new heuristic method, namely, adaptive simulated annealing with large-scale neighborhoods, to optimize truck and drone routes, where a scoring strategy is designed to dynamically adjust the selection weight of destroy operators and repair operators. Additionally, extensive experiments are conducted on several synthetic cases and one real case. The experimental results show that the proposed algorithm can effectively solve the large-scale target surveillance problem. Furthermore, the proposed cooperation of truck and drone mode brings new ideas and solutions to targets surveillance problems.
随着无人机技术的发展，无人机已被广泛应用于民用和军事领域，用于目标监测。然而，由于无人机的续航能力有限，大规模的目标监测任务仍面临着一些挑战。基于这一动机，我们提出了一种新的目标监测模式，即通过卡车和多架无人机的协同合作，扩大监测范围。该模式旨在合理规划卡车和无人机的路径，以最小化总成本。在这种模式下，载有多架无人机的卡车从基地出发，沿途释放小型无人机，对多个目标进行监测，回收所有无人机并返回基地。当无人机从卡车上发射时，它会对多个目标进行监测，并飞回卡车进行回收，同时考虑了无人机的能量消耗模型。为了帮助解决这个新问题，我们开发了一种新的启发式方法，即具有大规模邻域的自适应模拟退火算法，用于优化卡车和无人机的路径，其中设计了一种评分策略，以动态调整破坏算子和修复算子的选择权重。此外，我们还在几个合成案例和一个真实案例上进行了广泛的实验。实验结果表明，所提出的算法可以有效地解决大规模目标监测问题。此外，所提出的卡车和无人机协同合作模式为目标监测问题带来了新的思路和解决方案。
