We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic wind disturbances. In prior work on mobile sensor routing problems, sensors and their carrier are one integrated platform, and sensors are assumed to be able to take measurements at exactly desired locations. By contrast, airdropping the sensors onto the ground can introduce stochasticity in the landing locations of the sensors. We focus on addressing this stochasticity in sensor locations from the path-planning perspective. Specifically, we formulate the problem (Multi-UAV Sensor Drop) as a variant of the Submodular Team Orienteering Problem with one additional constraint on the number of sensors on each UAV. The objective is to maximize the Mutual Information between the phenomenon at Points of Interest (PoIs) and the measurements that sensors will take at stochastic locations. We show that such an objective is computationally expensive to evaluate. To tackle this challenge, we propose a surrogate objective with a closed-form expression based on the expected mean and expected covariance of the Gaussian Process. We propose a heuristic algorithm to solve the optimization problem with the surrogate objective. The formulation and the algorithms are validated through extensive simulations.

翻译：本文研究了在随机风力扰动条件下，利用能量受限的无人机集群投放不可移动传感器以监测任务区域的问题。在已有的移动传感器路径规划研究中，传感器与其运载平台通常构成一体化系统，且假设传感器能在精确的目标位置进行测量。相比之下，将传感器空投至地面会导致传感器落点位置存在随机性。本文从路径规划视角出发，重点解决传感器位置随机性问题。具体而言，我们将多无人机传感器投放问题建模为子模团队定向问题的一个变体，并增加了每架无人机负载传感器数量的约束条件。优化目标旨在最大化兴趣点物理现象与传感器在随机位置测量值之间的互信息。研究表明，该目标函数的计算代价极高。为应对这一挑战，我们提出一种基于高斯过程期望均值与期望协方差的闭式替代目标函数，并设计启发式算法以该替代目标求解优化问题。通过大量仿真实验验证了所提模型与算法的有效性。