0
Technical Brief

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

[+] Author and Article Information
Thomas J. Stastny

Department of Aerospace Engineering,
The University of Kansas,
Lawrence, KS 66045
e-mail: tstastny@ku.edu

Gonzalo A. Garcia

Department of Aerospace Engineering,
The University of Kansas,
Lawrence, KS 66045
e-mail: gagarcia@ku.edu

Shawn S. Keshmiri

Assistant Professor
Department of Aerospace Engineering,
The University of Kansas,
Lawrence, KS 66045
e-mail: keshmiri@ku.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 24, 2013; final manuscript received July 1, 2014; published online August 28, 2014. Assoc. Editor: Yongchun Fang.

J. Dyn. Sys., Meas., Control 137(1), 014503 (Aug 28, 2014) (10 pages) Paper No: DS-13-1282; doi: 10.1115/1.4028034 History: Received July 24, 2013; Revised July 01, 2014

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 of classical artificial 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 of evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and nonlinear 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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Meridian UAS flight test at NEEM, Greenland

Grahic Jump Location
Fig. 2

Waypoint generation logic (left) and guidance logic (right)

Grahic Jump Location
Fig. 3

Contour lines and vector definitions for a regular potential function and morphed potential function about a fixed obstacle: (a) vector field generated by the gradient of a morphed potential field, (b) potential cost for a morphed potential function about a fixed obstacle, and (c) parameters used: pobj = {750,500} ft ({229,152}m), Vobj = {0,0}ft/s, vro = vo = {169,0} ft/s({51.5,0} m), σ = 150 ft (45.7 m), φmax = π/3, and ds = Rmin/3

Grahic Jump Location
Fig. 4

Trajectory vector field. Parameters used: ξtraj = 0 rad, ξappmax = π/4 rad, ptraj = {0,100} ft ({0,30.5} m), and Ktraj = 0.05

Grahic Jump Location
Fig. 5

Moving point planning flow chart

Grahic Jump Location
Fig. 6

MPF and APF comparison in avoidance of a single obstacle

Grahic Jump Location
Fig. 7

States and control deflections for UAS 1 in single obstacle avoidance

Grahic Jump Location
Fig. 8

Simulation in an urban environment (top view)

Grahic Jump Location
Fig. 9

3D view of urban simulation. (Original image courtesy Google Earth™, 2013).

Grahic Jump Location
Fig. 10

States and control deflections for UAS 3 in simulation in an urban environment

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In