Model Free Barrier Functions via Implicit Evading Maneuvers

TL;DR

This paper introduces model-free barrier functions that use data-driven methods to improve collision avoidance, reducing unnecessary restrictions caused by traditional barrier functions in fixed-wing aircraft scenarios.

Contribution

It presents a novel data-driven approach to creating barrier functions that avoid overly conservative safety constraints in collision avoidance tasks.

Findings

Model-free barrier functions outperform traditional ones in avoiding unnecessary restrictions.

The approach effectively enables fixed-wing aircraft to maintain safety with better performance.

Simulation results demonstrate improved collision avoidance capabilities.

Abstract

This paper demonstrates that the safety override arising from the use of a barrier function can in some cases be needlessly restrictive. In particular, we examine the case of fixed-wing collision avoidance and show that when using a barrier function, there are cases where two fixed-wing aircraft can come closer to colliding than if there were no barrier function at all. In addition, we construct cases where the barrier function labels the system as unsafe even when the vehicles start arbitrarily far apart. In other words, the barrier function ensures safety but with unnecessary costs to performance. We therefore introduce model-free barrier functions which take a data driven approach to creating a barrier function. We demonstrate the effectiveness of model-free barrier functions in a collision avoidance simulation of two fixed-wing aircraft.