基于神经网络-粒子群优化的无人机在船只上着陆速度控制算法
Neural Network-PSO-based Velocity Control Algorithm for Landing UAVs on a Boat
AI总结：
提出一种基于粒子群优化和神经网络的PID参数自适应速度控制算法，用于无人机在移动船只上精确着陆，提高鲁棒性并适应多种任务需求。
摘要：
无人机（UAV）在自主水面艇（ASV）等移动平台上精确着陆，对于异构车辆的协同导航至关重要且具有挑战性，尤其是在GPS拒止环境中。无人机需要在ASV上的受限空间内着陆以获取能量补充，而ASV因风和水流而受到平移和旋转扰动。现有解决方案要么依赖高层航点导航，难以在不同速度的目标上稳健着陆，要么需要繁琐的手动调整控制器参数和昂贵的传感器进行目标定位。因此，我们提出一种自适应速度控制算法，利用粒子群优化（PSO）和神经网络（NN）在不同飞行高度和移动船只的不同速度下优化PID参数。PSO的代价函数包括无人机状态变化率和与目标的接近程度。神经网络进一步插值基于PSO的PID参数。所提出的方法在水黾六旋翼无人机设计上实现，不仅确保了精度，还增强了鲁棒性。此外，该NN-PSO可轻松适应各种任务需求。其实现精确着陆的能力扩展了其适用场景，包括但不限于救援任务、包裹递送和工作区检查。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
