Unmanned aerial vehicles (UAVs) offer dynamic trajectory control, enabling them to avoid obstacles and establish line-of-sight (LoS) wireless channels with ground nodes (GNs), unlike traditional ground-fixed base stations. This study addresses the joint optimization of scheduling and three-dimensional (3D) trajectory planning for UAV-assisted wireless data harvesting. The objective is to maximize the minimum uplink throughput among GNs while accounting for signal blockages and building avoidance. To achieve this, we first present mathematical models designed to avoid cuboid-shaped buildings and to determine wireless signal blockage by buildings through rigorous mathematical proof. The optimization problem is formulated as nonconvex mixed-integer nonlinear programming and solved using advanced techniques. Specifically, the problem is decomposed into convex subproblems via quadratic transform and successive convex approximation. Building avoidance and signal blockage constraints are incorporated using the separating hyperplane method and an approximated indicator function. These subproblems are then iteratively solved using the block coordinate descent algorithm. Simulation results validate the effectiveness of the proposed approach. The UAV dynamically adjusts its trajectory and scheduling policy to maintain LoS channels with GNs, significantly enhancing network throughput compared to existing schemes. Moreover, the trajectory of the UAV adheres to building avoidance constraints for its continuous trajectory, ensuring uninterrupted operation and compliance with safety requirements.

翻译：与传统地面固定基站不同，无人机凭借其动态轨迹控制能力，能够规避障碍物并与地面节点建立视距无线信道。本研究针对无人机辅助无线数据采集中的调度与三维轨迹规划联合优化问题展开研究。目标是在考虑信号阻塞与建筑规避的前提下，最大化所有地面节点中的最小上行链路吞吐量。为实现该目标，我们首先提出了用于规避长方体建筑并通过严格数学证明判定建筑导致无线信号阻塞的数学模型。该优化问题被表述为非凸混合整数非线性规划，并采用先进技术求解。具体而言，通过二次变换与逐次凸逼近将原问题分解为凸子问题。利用分离超平面方法与近似指示函数处理建筑规避与信号阻塞约束。随后，采用块坐标下降算法对这些子问题进行迭代求解。仿真结果验证了所提方法的有效性。无人机动态调整其轨迹与调度策略以维持与地面节点的视距信道，相较于现有方案显著提升了网络吞吐量。此外，无人机的连续飞行轨迹严格遵循建筑规避约束，确保了任务连续性与安全合规性。