MARLander: A Local Path Planning for Drone Swarms using Multiagent Deep Reinforcement Learning

Abstract-- 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. 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. Swarm drones, characterized by their collaborative behavior, are driving research due to their disruptive potential across industries like agriculture, construction, entertainment, and logistics [1], [2].