MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV

TL;DR

MorphoLander introduces a reinforcement learning-based system enabling a heterogeneous drone swarm to land precisely on a leader drone in rough terrains, enhancing exploration and operational efficiency in complex environments.

Contribution

This work presents a novel reinforcement learning approach for accurate drone landing on a leader in uneven terrains, integrating morphogenetic principles for swarm exploration.

Findings

Landing accuracy of 0.5 cm on even terrain

Landing accuracy of 2.35 cm on uneven terrain

Effective in complex, unstructured environments

Abstract

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.