MorphoLander: 適応型形態形成UAVへのドローン群の強化学習ベース着陸
MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV
不整地を移動できるリーダードローンに小型ドローン群が高精度で着陸するための強化学習アルゴリズムを開発し、平坦地で0.5cm、不整地で2.35cmの着陸精度を達成した。
著者: Sausar Karaf, Aleksey Fedoseev, Mikhail Martynov, Zhanibek Darush, Aleksei Shcherbak, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
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.
