Path Planning by Unmanned Air Vehicles to Goal States in Operational Environments

Abstract

Unmanned air vehicles (UAVs) are continually being tasked to operate with greater autonomy. One aspect of the autonomous control problem is generation of optimal sets of position waypoints and flight control sequences to effectively navigate a UAV through an operational environment. The environment may contain a series of obstacles that must be avoided and threats that are either avoided or engaged. It is assumed that the obstacles and threats contained in such an environment are known a priori. In this paper, an optimal control strategy is presented to navigate a UAV from an initial position to a series of predefined goal positions, each with attached state and temporal constraints. The number of goal states specified is the smallest number of states required for the UAV to complete the mission tasking at hand. Thus, the UAV must generate its own waypoints and control sequences that will navigate through the environment while avoiding certain threats and obstacles and reaching each goal state on-time. The UAV is assigned two-dimensional flight dynamics with limits on velocity, acceleration, and turn rate, as would be the case with a real-world vehicle. The optimal control strategy is based on minimizing a performance cost function that accounts for the listed spatial and temporal constraints. The control strategy is tested and assessed using three scenarios programmed into computer simulations, and the results are presented and discussed.