Path Planning of Unmanned Aerial Vehicles in a Dynamic Environment

The main goal of this research effort is to determine the optimal trajectory for an unmanned aerial vehicle (UAV) in a dynamic environment. A Model Predictive Control (MPC) approach is utilized to provide collision avoidance in view of pop-up threats and a random set of moving and stationary obstacles (no fly zones). The UAV path planning needs to adapt in near real-time to the dynamic nature of the operational scenario, and to react rapidly to updates in the situational awareness, given the vehicle’s maneuvering constraints. To achieve this objective, the UAV navigates from a given starting point to a desired target point via selected intermediate waypoints. The possible waypoints are geometrically obtained with additional waypoints placed near the vertices of each polygonshaped obstacle. The MPC optimizer minimizes a cost function at each control cycle using a nonlinear dynamic model of the situation with maneuvering constraints included. The MPC algorithm is selected because it can improve system performance while effectively handling constraints. However, the huge computational effort required for a complete nonlinear realization of the MPC algorithm renders infeasible a comprehensive real-time optimization for this application. To circumvent this problem, discrete-time Laguerre functions are used as basis functions to represent the control inputs instead of using their complete time histories. A representative scenario, consisting of five targets, five static obstacles, nine pop-up threats, and a large, moving no-fly zone, is used to demonstrate this algorithm. Results are presented based on MATLAB® simulation experiments using a UAV model that represents a RQ-7 Shadow 200 to demonstrate the effectiveness of this approach.