Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method

Park, Sanghyuk; Deyst, John; How, Jonathan P.

doi:10.2514/1.28957

Cited by 367 publications

(207 citation statements)

References 3 publications

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“…The outer loop guidance law follows closely the work developed in [Amidi and Thorpe, 1991], [Park et al, 2004], and [Park et al, 2007]. The original derivation is presented here for completeness.…”

Section: General Trackingmentioning

confidence: 99%

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Principles of Guidance, Navigation, and Control of UAVs

Elkaim

Lie

Egziabher

2014

Handbook of Unmanned Aerial Vehicles

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Two complete system architectures for a guidance, navigation and control solution of small UAVs is presented. These systems (developed at the University of California Santa Cruz and the University of Minnesota) are easily reconfigurable and are intended to support testbeds used in navigation, guidance and control research. The systems described both integrate a low cost inertial measurement unit, a GPS receiver, a triad of magnetometers to generate a navigation solution (position, velocity and attitude estimation) which, in turn, is used in the guidance and control algorithms. The navigation solution described is a 15 state Extended Kalman Filter which integrates the inertial sensor and GPS measurement to generate a high-bandwidth estimate of a UAV's state. Guidance algorithms for generating a flight trajectory based on waypoint definitions are also described. A PID controller which uses the navigation filter estimate and guidance algorithm to track a flight trajectory is detailed. The architecture: the hardware, software and algorithms is included for completeness. Hardware in the loop simulation and flight test results documenting the performance of these two systems is given. IntroductionIn current usage, the term Uninhabited Aerial Vehicles or UAVs refers to aircraft which fly without a human operator onboard. They span a wide range in size and complexity. The largest UAVs such as Predator or Globalhawk can weigh several thousand pounds and have wing spans on the order of 10 to 100 feet. At the other end of the size spectrum are UAVs whose maximum dimensions and mass are on the order of centimeters and grams, respectively. In general, these vehicles are being used or envisioned for use in operations where they serve primarily as a platform for a sensor payload. The sensor payload can be as simple as an Electro-Optical camera or as complex as synthetic aperture radar used for remote sensing.

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Section: General Trackingmentioning

confidence: 99%

“…35 to determine η and hence the commanded acceleration. For sufficiently small tracking errors, the L 1 guidance looks like a P D controller on lateral error [Park et al, 2007].…”

Section: Straight Line Trackingmentioning

confidence: 99%

Principles of Guidance, Navigation, and Control of UAVs

Elkaim

Lie

Egziabher

2014

Handbook of Unmanned Aerial Vehicles

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“…Each experiment consists of 1 or 2 autonomous vehicles and 1 human-driven vehicle. The autonomous vehicles are controlled through an off-board wrapper which receives waypoints from the threat-aware CL-RRT software (Section V-B) and steers using pure pursuit [18]. The human-driven vehicle is controlled through a wireless steering wheel [19], including acceleration and brake pedals.…”

Section: A Hardwarementioning

confidence: 99%

Threat-aware path planning in uncertain urban environments

Aoude

Luders

Levine

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper considers the path planning problem for an autonomous vehicle in an urban environment populated with static obstacles and moving vehicles with uncertain intents. We propose a novel threat assessment module, consisting of an intention predictor and a threat assessor, which augments the host vehicle's path planner with a real-time threat value representing the risks posed by the estimated intentions of other vehicles. This new threat-aware planning approach is applied to the CL-RRT path planning framework, used by the MIT team in the 2007 DARPA Grand Challenge. The strengths of this approach are demonstrated through simulation and experiments performed in the RAVEN testbed facilities.

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“…The lateral component is based on the work presented in [13,14]; however, we have also built upon this algorithm to encapsulate wind information in the guidance logic, rather than merely treating wind as an adaptive element for the control system. This addition to the control law has demonstrated robust performances for linear path following in strong winds.…”

Section: Guidance and Controlmentioning

confidence: 99%

Automating Human Thought Processes for a UAV Forced Landing

Eng

Mejias

Liu

et al. 2009

J Intell Robot Syst

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This paper describes the current status of a program to develop an automated forced landing system for a fixed-wing Unmanned Aerial Vehicle (UAV). This automated system seeks to emulate human pilot thought processes when planning for and conducting an engine-off emergency landing. Firstly, a path planning algorithm that extends Dubins curves to 3D space is presented. This planning element is then combined with a nonlinear guidance and control logic, and simulated test results demonstrate the robustness of this approach to strong winds during a glided descent. The average path deviation errors incurred are comparable to or even better than that of manned, powered aircraft. Secondly, a study into suitable multi-criteria decision making approaches and the problems that confront the decision-maker is presented. From this study, it is believed that decision processes that utilize human expert knowledge and fuzzy logic reasoning are most suited to the problem at hand, and further investigations will be conducted to identify the particular technique/s to be implemented in simulations and field tests. The automated UAV forced landing approach presented in this paper is promising, and will allow the progression of this technology from the development and simulation stages through to a prototype system.

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Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method

Cited by 367 publications

References 3 publications

Principles of Guidance, Navigation, and Control of UAVs

Principles of Guidance, Navigation, and Control of UAVs

Threat-aware path planning in uncertain urban environments

Automating Human Thought Processes for a UAV Forced Landing

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