Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2023 Jan 11;23(2):863. doi: 10.3390/s23020863.
In this paper, the problem of trajectory design for energy harvesting unmanned aerial vehicles (UAVs) is studied. In the considered model, the UAV acts as a moving base station to serve the ground users, while collecting energy from the charging stations located at the center of a user group. For this purpose, the UAV must be examined and repaired regularly. In consequence, it is necessary to optimize the trajectory design of the UAV while jointly considering the maintenance costs, the reward of serving users, the energy management, and the user service time. To capture the relationship among these factors, we first model the completion of service and the harvested energy as the reward, and the energy consumption during the deployment as the cost. Then, the is defined as the ratio of the reward to the cost of the UAV trajectory. Based on this definition, the trajectory design problem is formulated as an optimization problem whose goal is to maximize the deployment profitability of the UAV. To solve this problem, a foraging-based algorithm is proposed to find the optimal trajectory so as to maximize the deployment profitability and minimize the average user service time. The proposed algorithm can find the optimal trajectory for the UAV with low time complexity at the level of polynomial. Fundamental analysis shows that the proposed algorithm achieves the maximal deployment profitability. Simulation results show that, compared to Q-learning algorithm, the proposed algorithm effectively reduces the operation time and the average user service time while achieving the maximal deployment profitability.
本文研究了能量收集无人机（UAV）的轨迹设计问题。在所考虑的模型中，UAV 充当移动基站，为位于用户组中心的充电站收集能量，为地面用户提供服务。为此，必须定期检查和维修无人机。因此，有必要在联合考虑维护成本、服务用户的奖励、能量管理和用户服务时间的情况下，优化 UAV 的轨迹设计。为了捕捉这些因素之间的关系，我们首先将服务完成情况和收集到的能量建模为奖励，将部署期间的能量消耗建模为成本。然后，将定义为 UAV 轨迹的奖励与成本的比值。基于此定义，将轨迹设计问题表述为一个优化问题，其目标是最大化 UAV 的部署盈利能力。为了解决这个问题，提出了一种基于觅食的算法来寻找最优轨迹，以最大化部署盈利能力并最小化平均用户服务时间。所提出的算法可以在多项式级别的低时间复杂度下找到 UAV 的最优轨迹。基本分析表明，所提出的算法可以实现最大的部署盈利能力。仿真结果表明，与 Q-learning 算法相比，所提出的算法在实现最大部署盈利能力的同时，有效地减少了操作时间和平均用户服务时间。
