MorphoLander：基于强化学习的无人机群在自适应形态发生无人机上的着陆
MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV
AI总结：
本文提出MorphoLander异构无人机群系统，通过强化学习算法实现无人机在静止或移动领航机上的精确着陆，在平坦和不平坦地形下分别达到0.5厘米和2.35厘米的着陆精度，提升了复杂环境下的任务效率。
摘要：
本文聚焦于一种名为MorphoLander的新型机器人系统，它代表用于探索崎岖地形环境的异构无人机群。形态发生领航无人机能够在不平坦地形上着陆、穿越地形并保持水平姿态，以部署更小的无人机进行大范围区域探索。完成任务后，这些无人机返回并降落在MorphoGear的着陆垫上。研究开发了强化学习算法，用于无人机在领航机器人上的精确着陆，该领航机器人在任务期间保持静止或重新定位到新位置。进行了多项实验以评估所开发的着陆算法在平坦和不平坦地形条件下的性能。实验表明，所提出的系统在平坦地形条件下降落在领航无人机上时实现了0.5厘米的高着陆精度，在不平坦地形条件下为2.35厘米。MorphoLander有潜力显著提高在高度杂乱和非结构化环境中的工业检查、地震勘测和救援任务的效率。
英文摘要：
This paper focuses on a novel robotic system MorphoLander representing heterogeneous swarm of drones for exploring rough terrain environments. The morphogenetic leader drone is capable of landing on uneven terrain, traversing it, and maintaining horizontal position to deploy smaller drones for extensive area exploration. After completing their tasks, these drones return and land back on the landing pads of MorphoGear. The reinforcement learning algorithm was developed for a precise landing of drones on the leader robot that either remains static during their mission or relocates to the new position. Several experiments were conducted to evaluate the performance of the developed landing algorithm under both even and uneven terrain conditions. The experiments revealed that the proposed system results in high landing accuracy of 0.5 cm when landing on the leader drone under even terrain conditions and 2.35 cm under uneven terrain conditions. MorphoLander has the potential to significantly enhance the efficiency of the industrial inspections, seismic surveys, and rescue missions in highly cluttered and unstructured environments.
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。
