Rapid Uncertainty Propagation and Chance-Constrained Trajectory Optimization for Small Unmanned Aerial Vehicles

As the number of small Unmanned Aircraft Systems (sUAS) in the national airspace increases, it is becoming increasingly important to develop and implement a system for their air traffic management. Such an envisioned system will be capable of trajectory prediction, trajectory validation and trajectory optimization while accounting for uncertainty due to wind gusts, and in aircraft and flight control system parameters. This paper illustrates the use of covariance propagation based on linearized models for fast sUAS trajectory prediction and for trajectory optimization subject to chance constraints. The approach is demonstrated to be sufficiently accurate and much faster than Monte Carlo simulation techniques.