2025-01-31 マサチューセッツ工科大学(MIT
MIT engineers developed a training method for multiagent systems, such as large numbers of drones, that can guarantee their safe operation in crowded environments. Image: Courtesy of the researchers
＜関連情報＞
- https://news.mit.edu/2025/mit-engineers-help-multirobot-systems-stay-safety-zone-0131
- https://ieeexplore.ieee.org/abstract/document/10842511
GCBF+： 分散安全マルチエージェント制御のためのニューラルグラフ制御障壁関数フレームワーク GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control
Songyuan Zhang; Oswin So; Kunal Garg; Chuchu Fan
IEEE Transactions on Robotics Published:15 January 2025
DOI:https://doi.org/10.1109/TRO.2025.3530348
Abstract:
Distributed, scalable, and safe control of large-scale multi-agent systems is a challenging problem. In this paper, we design a distributed framework for safe multi-agent control in large-scale environments with obstacles, where a large number of agents are required to maintain safety using only local information and reach their goal locations. We introduce a new class of certificates, termed graph control barrier function (GCBF), which are based on the well-established control barrier function theory for safety guarantees and utilize a graph structure for scalable and generalizable distributed control of MAS. We develop a novel theoretical framework to prove the safety of an arbitrary-sized MAS with a single GCBF. We propose a new training framework GCBF+ that uses graph neural networks to parameterize a candidate GCBF and a distributed control policy. The proposed framework is distributed and is capable of taking point clouds from LiDAR, instead of actual state information, for real-world robotic applications. We illustrate the efficacy of the proposed method through various hardware experiments on a swarm of drones with objectives ranging from exchanging positions to docking on a moving target without collision. Additionally, we perform extensive numerical experiments, where the number and density of agents, as well as the number of obstacles, increase. Empirical results show that in complex environments with agents with nonlinear dynamics (e.g., Crazyflie drones), GCBF+ outperforms the hand-crafted CBF-based method with the best performance by up to $20\%$ for relatively small-scale MAS with up to 256 agents, and leading reinforcement learning (RL) methods by up to $40\%$ for MAS with 1024 agents. Furthermore, the proposed method does not compromise on the performance, in terms of goal reaching, for achieving high safety rates, which is a common trade-off in RL-based methods.
