Collision and Obstacle Avoidance in Unmanned Aerial Systems Using Morphing Potential Field Navigation and Nonlinear Model Predictive Control

Abstract

This paper presents a novel approach to collision and obstacle avoidance in fixedwing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation of classical artificial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering sixdegreesoffreedom (6DOF) dynamic characteristics and constraints of fixedwing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation of evasive paths during abrupt encounters. The timevarying waypoint horizons output from the navigation unit are integrated into a combined guidance and nonlinear model predictive control scheme. Realtime avoidance capabilities are demonstrated in full nonlinear 6DOF simulation of a large unmanned aircraft showcasing evasion efficiency with respect to classical methods and collision free operation in a congested urban scenario.