Probabilistic risk assessment and mitigation for uas safety and traffic management

Abstract

A probabilistic approach to help future UAS safety and traffic management is proposed. A probabilistic risk-based operational safety bound is first proposed. This will allow UAV to maintain the deviation from the trajectory plan within a "buffer" zone, and keep away from obstacles. Uncertainty quantification, Monte Carlo simulation, and coordinate transformation techniques will be used. Additionally, an algorithm integrating reinforcement learning and the proposed operational safety bound will be developed which is used for collision avoidance and trajectory planning. This will provide a learning ability to UAVs in order to adapt their behavior to changing environments. Also, large-scale UAV management will be studied by using multi-agent generative adversarial imitation learning.