# Distributed Safe Navigation of Multi-Agent Systems using Control Barrier Function-Based Optimal Controllers

arXiv: https://arxiv.org/abs/2402.06195 · HTML: https://mlsanity.com/paper/2402.06195/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Domain Adaptive Safety Filters via Deep Operator Learning](https://mlsanity.com/paper/2410.14528/) (2024)

## Abstract (arXiv)

This paper proposes a distributed controller synthesis framework for safe navigation of multi-agent systems. We leverage control barrier functions to formulate collision avoidance with obstacles and teammates as constraints on the control input for a state-dependent network optimization problem that encodes team formation and the navigation task. Our algorithmic solution is valid for general nonlinear control dynamics and optimization problems. The resulting controller is distributed, satisfies the safety constraints at all times, and is asymptotically optimal. We illustrate its performance in a team of differential-drive robots in a variety of complex environments, both in simulation and in hardware.