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 fixed-wing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation o f classical artifi cial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering six-degrees-of-freedom (6DOF) dynamic characteristics and constraints of fixed-wing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation o f evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and non linear model predictive control scheme. Real-time 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.

Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics

“…Augmentation of the NMPC internal model to include the guidance formulation has also previously been explored [21], [13], [12]; however, in these approaches, the analytic guidance law was used to generate attitude references within the control horizon, commands we wish our high-level NMPC to allocate itself. We therefore propose in this work to leave the control allocation open-ended for the nonlinear optimization to solve in real-time, while providing the NMPC with an error angle in the objective which, when minimized, results in convergence to the path.…”

“…Other works have considered lowerlevel formulations, either incorporating all objectives from obstacle avoidance to actuator penalty directly [10], focusing on low-level states only, e.g. for deep-stall landing [11], or augmenting the internal low-level model with guidance logic [12], [13].…”

A strategy for safe 3D navigation of non-holonomic robots among moving obstacles

“…Few papers studied the much more difficult case of collision free 3D navigation; however, various methods of collision free navigation were recently proposed for various types of underwater vehicles 7,8 and unmanned aircraft in refs. [9][10][11][12][13][14][15] . Furthermore, effective algorithms of navigation of underwater and surface vehicles with uncertain dynamics were developed in refs.…”

Hybrid Planning and Control for Multiple Fixed-Wing Aircraft under Input Constraints

“…. Convergence Analysis of mode q i4 (Go-towards-Goal) Under the effect of control law(17), agent i moves towards its goal location r gi .…”