MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm

--For effective multi-agent trajectory planning, it is important to consider lightweight communication and its potential asynchrony. This paper presents a distributed trajectory planning algorithm for a quadrotor swarm that operates asynchronously and requires no communication except during the initial planning phase. T o effectively ensure these points, we build two main modules: coordination state updater and trajectory optimizer . The coordination state updater computes waypoints for each agent toward its goal and performs subgoal optimization while considering deadlocks, as well as safety constraints with respect to neighbor agents and obstacles. Then, the trajectory optimizer generates a trajectory that ensures collision avoidance even with the asynchronous planning updates of neighboring agents. We provide a theoretical guarantee of collision avoidance with deadlock resolution and evaluate the effectiveness of our method in complex simulation environments, including random forests and narrow-gap mazes. Additionally, to reduce the total mission time, we design a faster coordination state update using lightweight communication. Lastly, our approach is validated through extensive simulations and real-world experiments with cluttered environment scenarios. Index T erms --Path Planning for Multiple Mobile Robots, Collision A voidance, Distributed Robot Systems. HE compactness of quadrotor drones enables the operation of multi-agent systems in cluttered environments. While small teams of drones can be manually controlled by human pilots, large-scale swarms require autonomous coordination, where multi-agent trajectory planning (MA TP) serves as a critical component. Over the past decade, MA TP has been extensively studied, leading to its adoption in various applications, such as surveillance [1], inspection [2], and transportation [3]. Many existing MA TP frameworks rely on synchronous coordination, where agents repeatedly exchange information to maintain consistency during planning and execution [4]. However, as the number of agents increases, the communication load grows significantly, often resulting in message delays and packet losses. The author is with AI Institute of Seoul National University, Seoul, South Korea, and Carnegie Mellon University, Pittsburgh, P A, USA (e-mail: yunwoo333@gmail.com) The author is with the Department of Mechanical System Design Engineering, Seoul National University of Science and Technology (SEOUL-TECH), Seoul, South Korea (e-mail: jungwonpark@seoultech.ac.kr)