MARLander: マルチエージェント深層強化学習によるドローン群の局所経路計画
MARLander: A Local Path Planning for Drone Swarms using Multiagent Deep Reinforcement Learning
マルチエージェント深層強化学習を用いてドローン群を移動する目標地点へ精密に着陸させる手法を提案し、実機実験でPID+APFを上回る着陸精度を達成した。
著者: Demetros Aschu, Robinroy Peter, Sausar Karaf, Aleksey Fedoseev, Dzmitry Tsetserukou
分類: cs.RO, cs.MA
原文アブストラクト
Achieving safe and precise landings for a swarm of drones poses a significant challenge, primarily attributed to conventional control and planning methods. This paper presents the implementation of multi-agent deep reinforcement learning (MADRL) techniques for the precise landing of a drone swarm at relocated target locations. The system is trained in a realistic simulated environment with a maximum velocity of 3 m/s in training spaces of 4 x 4 x 4 m and deployed utilizing Crazyflie drones with a Vicon indoor localization system. The experimental results revealed that the proposed approach achieved a landing accuracy of 2.26 cm on stationary and 3.93 cm on moving platforms surpassing a baseline method used with a Proportional-integral-derivative (PID) controller with an Artificial Potential Field (APF). This research highlights drone landing technologies that eliminate the need for analytical centralized systems, potentially offering scalability and revolutionizing applications in logistics, safety, and rescue missions.
