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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.

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Figures

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Fig. 1

Meridian UAS flight test at NEEM, Greenland

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Fig. 2

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

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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

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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

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Fig. 5

Moving point planning flow chart

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Fig. 6

MPF and APF comparison in avoidance of a single obstacle

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Fig. 7

States and control deflections for UAS 1 in single obstacle avoidance

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Fig. 8

Simulation in an urban environment (top view)

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Fig. 9

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

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Fig. 10

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

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