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

TL;DR

The paper tackles multi-agent trajectory planning for quadrotor swarms under minimal communication. It introduces MC-Swarm, an asynchronous, distributed MATP framework built on a coordination state updater and a trajectory optimizer, with no in-flight communication in the no-communication variant and a lightweight communication variant to speed coordination. The approach provides theoretical guarantees for collision avoidance and deadlock resolution, demonstrated through extensive simulations and real-world hardware experiments in dense obstacle environments. The results show that MC-Swarm can achieve high success rates and shorter mission times compared to state-of-the-art methods, while maintaining safety in cluttered scenarios. The work offers a practical pathway to scalable, robust swarm navigation with reduced communication overhead and optional fast coordination via limited messaging.

Abstract

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. Moreover, our algorithm guarantees no deadlock under asynchronous updates and absence of communication during flight. To 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.

Figures (11)

• Figure 1: Demonstration of a goal-reaching mission with eight quadrotors in a narrow-gap environment.
• Figure 2: Planning process of the proposed algorithm executed by each agent
• Figure 3: Collision constraints used in the proposed algorithm. The triangles are estimated positions, and the circles are subgoals at the previous state update step. The gray region represents static obstacles, and the color-shaded regions denote the feasible region that satisfies all collision constraints.
• Figure 4: Comparison of trajectory planning when using the most recent collision constraint (naive method) and the conservative constraint (proposed method). The color-shared region represent collision constraint used for trajectory planning, and $t_{i}$ and $t_{j}$ are time when agents $i$ and $j$ update their trajectory, respectively.
• Figure 5: Example of blocking agents. The square dots, circle dots, and color-shaded regions represent waypoints, subgoals, and agents' feasible regions, respectively.

Theorems & Definitions (18)

• Lemma 1
• proof
• Lemma 2
• proof
• Lemma 3
• proof
• Lemma 4
• proof
• Theorem 1
• proof