  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.
