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.

翻译：本文聚焦于新型机器人系统MorphoLander，该异构无人机群专为崎岖地形环境探索而设计。形态引导无人机能够在非平坦地形上着陆、行进并保持水平姿态，从而部署小型无人机进行大范围区域探查。待任务完成后，这些小型无人机返回并降落在MorphoGear的降落平台上。我们开发了强化学习算法，用于实现无人机群在（执行任务期间保持静止或重新定位的）引导无人机上的精确降落。通过多组实验评估了所提降落算法在平坦与非平坦地形条件下的性能。实验结果表明，该算法在平坦地形条件下着陆于引导无人机时的精度达0.5厘米，非平坦地形条件下为2.35厘米。MorphoLander系统有望显著提升高度杂乱非结构化环境中的工业巡检、地震勘测及救援任务的执行效率。