Algorithms for 3D UAV Path Generation and Tracking

Ambrosino, G.; Ariola, M.; Ciniglio, Umberto; Corraro, Federico; Pironti, A.; Virgilio, M.

doi:10.1109/cdc.2006.377555

Cited by 52 publications

(20 citation statements)

References 7 publications

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“…In each case, the aircraft initial altitude and wind conditions were allowed to vary, while the initial and final Fig. 8 Using the speed polar diagram while gliding in winds (Image obtained from [12] aircraft headings and positions were kept constant. A Monte Carlo simulation using a larger input set will be performed at a later date.…”

Section: Resultsmentioning

confidence: 99%

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Automating Human Thought Processes for a UAV Forced Landing

Eng

Mejias

Liu

et al. 2009

Selected Papers From the 2nd International Symposium on UAVs, Reno, Nevada, U.S.A. June 8–10, 2009

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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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Section: Resultsmentioning

confidence: 99%

“…Once the radii are determined, the optimal 2D path is obtained with a geometrical construction adapted from [12]. Initially, two circles with radii R 0 are drawn containing the starting point P 0 and a vector pointing along the aircraft's initial heading ψ 0 (Figure 3a).…”

Section: Path Planningmentioning

confidence: 99%

Automating Human Thought Processes for a UAV Forced Landing

Eng

Mejias

Liu

et al. 2009

Selected Papers From the 2nd International Symposium on UAVs, Reno, Nevada, U.S.A. June 8–10, 2009

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“…We used for the path generation in the Alignment phase the algorithm, developed by CIRA and University "Federico II" of Naples, described in detail 12,14 . This Alignment algorithm is based on the Dubins method 13 to generate a planar trajectory satisfying initial and terminal conditions, specified in terms of position and heading…”

Section: Fully Adaptive Autonomous Landing Algorithmmentioning

confidence: 99%

Flight Testing of a Fully Adaptive Algorithm for Autonomous Fixed Wing Aircrafts Landing

Lellis

Vito

Marrone

et al. 2012

Infotech@Aerospace 2012

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This paper presents the flight test results of a fully adaptive algorithm for autonomous fixed wing aircrafts landing developed by CIRA, the Italian Aerospace Research Centre. The algorithm is designed and implemented in the framework of a complete autonomous guidance system, worked out by CIRA, able to allow autonomous way-points navigation, autonomous landing and autonomous collision avoidance for fixed wing aircrafts. The algorithm presented in the paper is designed to perform a fully adaptive autonomous landing starting from any point of the three dimensional space, based on the use of the DGPS/AHRS technology. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line landing trajectory re-planning, fully autonomy from pilot inputs, weakly instrumented landing runway, ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions. The flight tests have been conducted at an airfield in Caserta, in the south of Italy, close the CIRA. The paper is structured into several paragraphs describing the algorithm designed for the autolanding maneuver, the control system architecture and the methodologies developed in order to safely manage the possible presence of failures and/or unfavorable weather conditions, the preliminary results of the real time validation with hardware in the loop simulation and, finally, the performances achieved by using the CIRA experimental flying platform, with reference to the real flight experiments. Nomenclature X = position along X-Runway axis ) ( X V X = inertial velocity profile along X-Runway axis ) ( X V Z = inertial velocity profile along Z-Runway axis ) ( X H = position profile along Z-Runway axis 0 H = initial position along Z-Runway axis 0 X = initial position along X-Runway axis 0 X V = initial inertial velocity profile along X-Runway axis F H = desired final position along Z-Runway axis F X = desired final position along X-Runway axis XF V = desired final inertial velocity profile along X-Runway axis ZF V = desired final inertial velocity profile along Z-Runway axis

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“…Once the waypoints and path elements are planned, a suitable path tracking algorithm must be found. An example of a stable non-linear tracking controller is presented by Ambrosino et al (2006). As an elegant way to determine a suitable flight path and a vehicle guidance law at the same time, Lawrence et al (2008) proposed a vector field technique that is shown to have Lyapunov stability properties.…”

Section: Localisation and Navigationmentioning

confidence: 99%

Underwater Robot Swarms: Challenges and Opportunities

2013

Handbook of Collective Robotics

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Algorithms for 3D UAV Path Generation and Tracking

Cited by 52 publications

References 7 publications

Automating Human Thought Processes for a UAV Forced Landing

Automating Human Thought Processes for a UAV Forced Landing

Flight Testing of a Fully Adaptive Algorithm for Autonomous Fixed Wing Aircrafts Landing

Underwater Robot Swarms: Challenges and Opportunities

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