Trajectory generation for a UAV in urban terrain, using nonlinear MPC

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Abstract
This paper describes a receding-horizon optimal control scheme for autonomous trajectory generation and flight control of an unmanned air vehicle in urban terrain. In such environments, the mission objective or terrain may be dynamic, and the vehicle may change dynamics mid-flight due to sensor or actuator failure; thus off-line pre-planned flight trajectories axe limiting and insufficient. This technology is aimed at supporting guidance and control for future missions that will require vehicles with increased autonomy in dangerous situations and with tight maneuvering and operational capability e.g., missions in urban environments. A Model Predictive Control (MPC) scheme is described here that navigates a vehicle with nonlinear dynamics through a vector of known way-points to a goal, and manages constraints. In this MPC-based approach to trajectory planning with constraints, a feedforward nominal trajectory is used to convert the nonconvex, nonlinear optimal control problem into a time-varying linear, convex optimization or quadratic programming problem. The nonconvex, admissible path space is converted to a sequence of overlapping, convex spaces. The feedforward control that produces the nominal trajectory is found from the vehicle's differentially flat outputs. MPC is used to determine the optimal perturbations to the nominal control that will suitably navigate the vehicle through a constrained input/output space while minimizing actuation effort. Simulation results with a non-real-time, online MPC controller for a UAV in a planar urban terrain are included to support the proposed approach.

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