Online Control Barrier Functions for Decentralized Multi-Agent Navigation

Understand

Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain.

• The resulting navigation performance, however, is highly sensitive to the underlying hyperparameters.
• Traditional approaches consider fixed CBFs (where parameters are tuned apriori), and hence, typically do not perform well in cluttered and highly dynamic environments: conservative parameter values can lead to inefficient agent trajectories, or even failure to reach goal positions, whereas aggressive parameter values can lead to infeasible controls.
• To overcome these issues, in this paper, we propose online CBFs, whereby hyperparameters are tuned in real-time, as a function of what agents perceive in their immediate neighborhood.