Live Green Eric Frew and Sriram Sankaranarayanan University of - PowerPoint PPT Presentation

Run-time Assurance for UAVs using Stochastic Modeling and Reachability Analysis Hansol Yoon, Yi Chou, Live Green Eric Frew and Sriram Sankaranarayanan University of Colorado, Boulder FMCAD’18 Student Forum · October 31, 2018 * Unmanned Aerial Vehicles FMCAD 2018

Motjvatjon & Objectjve • UAVs are increasingly common in crowded urban spaces with risks to life and property due to disturbances such as wind. • Forecasts future UAVs positions to predict and avoid collisions. • Quantify risk of collisions with fixed obstacles. source: ABC news source: Global Trade Magazine FMCAD 2018

Predictjve Monitoring Framework <Architecture for the framework> Core Position Model Data Flight Flight UAV UAV Data Data Platform Platform Future Velocity Forecast sub-model Warnings, Control (future work) Deviation/Disturbance sub-model Model Model Model Reachability Reachability Inference Inference Analysis Analysis Waypoint Waypoint Guidance Guidance Future Acceleration Algorithm Algorithm Waypoints FMCAD 2018

Collision Predictjon Results ■ Evaluation on Talon UAV Flight Test Data Real SAFE COLLISION Prediction SAFE 94 0 COLLISION 5 94 NOT SURE 1 6 T e s t c o n d i t i o n s : 1 . P r o b a b i l i t y o f c o l l i s i o n > = 0 . 4 2 . P r e d i c t i o n t i m e h o r i z o n : 2 5 s e c s 3 . T h e d i s t a n c e o f t h e c e n t e r o f a n o b s t a c l e Square: Obstacle - 2 5 m f o r S A F E t e s t s Red line: Ground truth trajectory - 0 m f o r C O L L I S I O N t e s t s Blue lines: Predicted trajectories 4 . Wi n d : 3 m / s FMCAD 2018