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Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms likeAutonomous Surface Vehicles (ASVs) is both important and challenging,especially in GPS-denied environments, for collaborative navigation ofheterogeneous vehicles. UAVs need to land within a confined space onboard ASVto get energy replenishment, while ASV is subject to translational androtational disturbances due to wind and water flow. Current solutions eitherrely on high-level waypoint navigation, which struggles to robustly land onvaried-speed targets, or necessitate laborious manual tuning of controllerparameters, and expensive sensors for target localization. Therefore, wepropose an adaptive velocity control algorithm that leverages Particle SwarmOptimization (PSO) and Neural Network (NN) to optimize PID parameters acrossvarying flight altitudes and distinct speeds of a moving boat. The costfunction of PSO includes the status change rates of UAV and proximity to thetarget. The NN further interpolates the PSO-founded PID parameters. Theproposed method implemented on a water strider hexacopter design, not onlyensures accuracy but also increases robustness. Moreover, this NN-PSO can bereadily adapted to suit various mission requirements. Its ability to achieveprecise landings extends its applicability to scenarios, including but notlimited to rescue missions, package deliveries, and workspace inspections.

Neural Network-PSO-based Velocity Control Algorithm for Landing UAVs on a Boat