Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent ControlDownload PDF

Published: 30 Aug 2023, Last Modified: 16 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: Distributed control, Control barrier functions, Graph neural networks
TL;DR: We introduce a new notion of GCBF to encode inter-agent collision and obstacle avoidance in control for large-scale multi-agent systems with LiDAR-based observations, and jointly learn it with a distributed controller using GNNs.
Abstract: We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem of collision avoidance, scalability, and generalizability by introducing graph control barrier functions (GCBFs) for distributed control. The newly introduced GCBF is based on the well-established CBF theory for safety guarantees but utilizes a graph structure for scalable and generalizable decentralized control. We use graph neural networks to learn both neural a GCBF certificate and distributed control. We also extend the framework from handling state-based models to directly taking point clouds from LiDAR for more practical robotics settings. We demonstrated the efficacy of GCBF in a variety of numerical experiments, where the number, density, and traveling distance of agents, as well as the number of unseen and uncontrolled obstacles increase. Empirical results show that GCBF outperforms leading methods such as MAPPO and multi-agent distributed CBF (MDCBF). Trained with only $16$ agents, GCBF can achieve up to $3$ times improvement of success rate (agents reach goals and never encountered in any collisions) on $<500$ agents, and still maintain more than $50\%$ success rates for $>\!1000$ agents when other methods completely fail.
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