Safe Quadrotor Navigation using Composite Control Barrier Functions

2025-02-06

AI Summary

This work addresses the real-time collision avoidance challenge for multirotor UAVs operating in complex, cluttered indoor and outdoor environments. Methodologically, we propose a safety-filtering framework based on Composite Control Barrier Functions (CCBFs), introducing— for the first time—a unified CCBF that integrates hundreds of positional constraints within a third-order nonlinear dynamical model; this ensures recursive feasibility while driving the measure of the infeasible set to zero. The framework tightly couples onboard LiDAR perception, nonlinear optimization, and closed-loop control to achieve millisecond-scale online safety correction. Experimental results demonstrate guaranteed flight safety in densely cluttered static and dynamic obstacle scenarios, full compatibility with diverse nominal controllers—including adversarial policies—and substantial improvements in both robustness and real-time performance. The proposed approach provides a provably safe, general-purpose solution for autonomous navigation in high-density environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Ensures collision avoidance for multirotor aerial robots
• Uses Composite Control Barrier Functions for safety
• Scalable to complex scenes with numerous obstacles

Innovation

• Composite Control Barrier Function
• Third-order nonlinear dynamics
• Onboard LiDAR integration