Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms like Autonomous Surface Vehicles (ASVs) is both important and challenging, especially in GPS-denied environments, for collaborative navigation of heterogeneous vehicles. UAVs need to land within a confined space onboard ASV to get energy replenishment, while ASV is subject to translational and rotational disturbances due to wind and water flow. Current solutions either rely on high-level waypoint navigation, which struggles to robustly land on varied-speed targets, or necessitate laborious manual tuning of controller parameters, and expensive sensors for target localization. Therefore, we propose an adaptive velocity control algorithm that leverages Particle Swarm Optimization (PSO) and Neural Network (NN) to optimize PID parameters across varying flight altitudes and distinct speeds of a moving boat. The cost function of PSO includes the status change rates of UAV and proximity to the target. The NN further interpolates the PSO-founded PID parameters. The proposed method implemented on a water strider hexacopter design, not only ensures accuracy but also increases robustness. Moreover, this NN-PSO can be readily adapted to suit various mission requirements. Its ability to achieve precise landings extends its applicability to scenarios, including but not limited to rescue missions, package deliveries, and workspace inspections.

翻译：无人机在自主水面船等移动平台上精确降落，对于异构车辆的协同导航至关重要且极具挑战性，尤其在无GPS环境下。无人机需在船体有限空间内降落以获取能量补给，而自主水面船会因风和水流产生平移与旋转扰动。现有方案要么依赖高层航点导航，难以稳定降落在变速目标上；要么需要繁琐的手动参数整定及昂贵的传感器进行目标定位。为此，我们提出一种自适应速度控制算法，利用粒子群优化和神经网络优化不同飞行高度及移动船速下的PID参数。粒子群优化的代价函数包含无人机状态变化率及与目标的接近程度，神经网络进一步插值粒子群优化得到的PID参数。该方法在水黾六旋翼无人机上实现，不仅保证了精度，还增强了鲁棒性。此外，该神经网络-粒子群优化可便捷适配不同任务需求。其精确降落能力拓展了其应用场景，包括但不限于救援任务、包裹递送及工作空间巡检。