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：无人飞行器（UAV）在自主水面艇（ASV）等移动平台上的精确降落对于异构飞行器的协同导航至关重要，尤其是在无GPS环境中具有重要挑战性。UAV需在ASV受限空间内着陆以获取能量补给，而ASV在水流和风力作用下会产生平移与旋转扰动。现有方案或依赖高层航点导航（难以鲁棒降落在变速目标上），或需人工繁琐调整控制器参数并配备昂贵的目标定位传感器。为此，我们提出一种自适应速度控制算法，利用粒子群优化（PSO）与神经网络（NN）在不同飞行高度和移动船只速度下优化PID参数。PSO的代价函数包含UAV状态变化率及与目标接近程度，NN进一步插值基于PSO的PID参数。该算法在水黾六旋翼飞行器平台上实现，不仅确保了着陆精度，还增强了鲁棒性。此外，NN-PSO可便捷适配不同任务需求。其精准着陆能力拓展了在救援任务、包裹投递及工作空间巡检等场景中的适用性。