Obstacle Avoidance for Multirotor Urban Air Mobility via Prediction-Based Control Barrier Functions

This paper applies the recently developed Prediction-Based Control Barrier Functions (PB-CBFs) to the obstacle avoidance problem for multirotor air taxis in Urban Air Mobility (UAM).Unlike conventional Control Barrier Functions (CBFs), PB-CBFs incorporate escape path predictions into the formulation, facilitating safe controller design for dynamical systems with high relative degree and enabling safety under strict control constraints.We first review the PB-CBF framework, then formulate the safety requirements specific to the collision avoidance problem and derive the corresponding invariance conditions.Finally, we validate our approach through simulation of the obstacle avoidance scenario, demonstrating the efficacy of PB-CBFs in ensuring safety in UAM operations and providing additional insight into the mechanism by which predictions are leveraged to enforce safety.