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：本文聚焦于一种新型机器人系统MorphoLander，该系统由异构无人机群组成，用于探索崎岖地形环境。形态生成领航无人机能够在不平整地形上着陆、穿越并保持水平姿态，从而部署更小型无人机进行大范围区域勘探。完成指定任务后，这些无人机返回并降落至MorphoGear的着陆平台上。本文开发了一种强化学习算法，用于实现无人机在领航机器人（在执行任务期间保持静止或移动至新位置）上的精确降落。通过多组实验，分别在平整与不平整地形条件下评估了所开发降落算法的性能。实验结果表明，在平整地形条件下，无人机在领航机器人上的降落精度高达0.5厘米；在不平整地形条件下，精度为2.35厘米。MorphoLander有望显著提升高度杂乱与非结构化环境中的工业巡检、地震勘探及救援任务的执行效率。