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

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 linear velocity of 3 m/s in training spaces of 4 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>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 high-lights drone landing technologies that eliminate the need for analytical centralized systems, potentially offering scalability and revolutionizing applications in logistics, safety, and rescue missions.