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

TL;DR

This paper introduces an adaptive velocity control algorithm combining Neural Networks and Particle Swarm Optimization to enable UAVs to land precisely on moving boats in GPS-denied environments, enhancing robustness and adaptability.

Contribution

The novel integration of NN and PSO for real-time PID parameter optimization improves UAV landing accuracy and robustness on moving targets without relying on expensive sensors.

Findings

Successful implementation on a hexacopter water strider design

Enhanced landing accuracy and robustness in dynamic conditions

Adaptability to various mission scenarios

Abstract

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.