# Safe Quadrotor Navigation using Composite Control Barrier Functions

arXiv: https://arxiv.org/abs/2502.04101 · HTML: https://mlsanity.com/paper/2502.04101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [CN-CBF: Composite Neural Control Barrier Function for Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2603.06921/) (2026)
- [DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles](https://mlsanity.com/paper/2609.38873/) (2026)

## Abstract (arXiv)

This paper introduces a safety filter to ensure collision avoidance for multirotor aerial robots. The proposed formalism leverages a single Composite Control Barrier Function from all position constraints acting on a third-order nonlinear representation of the robot's dynamics. We analyze the recursive feasibility of the safety filter under the composite constraint and demonstrate that the infeasible set is negligible. The proposed method allows computational scalability against thousands of constraints and, thus, complex scenes with numerous obstacles. We experimentally demonstrate its ability to guarantee the safety of a quadrotor with an onboard LiDAR, operating in both indoor and outdoor cluttered environments against both naive and adversarial nominal policies.