Multicriteria Parametrical Identification of the Parafoil-Load Delivery System

Yakimenko, Oleg A.; Statnikov, Roman B.

doi:10.2514/6.2005-1664

Cited by 17 publications

(12 citation statements)

References 23 publications

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“…Therefore, each camera contributes two nonlinear equations of the form (2). That is where it follows from that to resolve the original problem for three components of the vector P we need to have at least two cameras (N≥2).…”

Section: Payload Position Estimationmentioning

confidence: 99%

Status on Video Data Reduction and Air Delivery Payload Pose Estimation

Yakimenko¹,

Berlind²,

Albright³

2007

19th AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar

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The paper discloses a current status of the development and evaluation of an autonomous payload tracking capability for determining time, state and attitude information (TSPI) for all types of airdrop loads. This automated capability of accurately acquiring TSPI data is supposed to radically reduce the labor time and eliminate man-in-the-loop errors. The paper starts with the problem formulation and then reviews the developed software. The algorithms rely on commercial off-the-shelf feature-based video-data processing software adopted for obtaining TSPI. Having the frame coordinates of the centroid of a tracking item available from no less than three ground stationary surveyed cameras at each instant during a descent together with these cameras azimuth and elevation information allows solving the position estimation problem. If more known-geometry features of the airdrop load can reliably be extracted, the pose (position and attitude) estimation would also be possible. The paper primarily addresses the status of the payload's position estimation portion providing examples of processing video data from up to six cameras. Yet, it also discusses the applicability of more recent computer-vision algorithm, based on establishing and tracking multiple scale-invariant keypoints. The paper ends with conclusions and suggestions for the further development.

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Section: Payload Position Estimationmentioning

confidence: 99%

Status on Video Data Reduction and Air Delivery Payload Pose Estimation

Yakimenko¹,

Berlind²,

Albright³

2007

19th AIAA Aerodynamic Decelerator Systems Technology Conference and Seminar

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“…The references [1][2][3][6][7][8][9][10]14] discuss similar multicriteria problems of improving a prototype of vehicles, machine tools and their units, parafoilload delivery system, controllable descending system, etc. For example, after road, bench, full-scale and other tests, improving some important characteristics of a car is necessary.…”

Section: Introductionmentioning

confidence: 99%

Visualization Tools for Multicriteria Analysis of the Prototype Improvement Problem

Statnikov

Anıl

Bordetsky

et al. 2007

2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making

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-One of the basic engineering optimization problems is the problem of improving a prototype. This problem is constantly encountered by industrial and academic organizations that produce and design various objects (e.g., motor vehicles, machine tools, ships, and aircraft). This paper presents an approach for improving a prototype by construction of the feasible and Pareto sets while performing multicriteria analysis. We introduce visualization methods that facilitate constructing the feasible and Pareto sets. Using these techniques, an expert can correctly state and solve the problem under consideration in a series of dialogs with the computer. Finally, we present a case study of applying these methods to a problem of improving a prototype of the ship.

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“…In this case, the system identification procedure described here could be used to develop an approximate model based on GPS alone which could then be refined with whatever additional sensor information is available. Depending on the available sensor data, this refinement could be performed using similar methods to the existing airdrop identification methods mentioned previously [4][5][6][7][8][9][10][11]. Another possibility is that the model made from GPS data using the proposed procedure could be simply augmented to capture unmodeled dynamics observed in the additional sensor channels using more general system identification techniques such as those recently developed by Majji et al [15,16].…”

mentioning

confidence: 99%

“…A number of efforts have applied aerodynamic parameter identification to airdrop systems [4][5][6][7][8][9][10][11]. Jann et al describe the development of a highly instrumented parafoil and payload research platform, ALEX, incorporating a Global Positioning System (GPS) receiver, magnetometers, rate gyros, accelerometers, air data probes, and video cameras to address the problem of generating enough data for system identification [4].…”

mentioning

confidence: 99%

“…Kothandaraman and Rotea described the use of a computationally efficient method to identify coefficients for a 6-DOF circular parachute model assuming perfect knowledge of the winds [10]. Yakimenko and Statnikov presented a method for identifying aerodynamic coefficients of an 8-DOF parafoil model using a multicriteria optimization method beginning with a parameter space investigation to help address the problems of local minima and infeasible regions in the parameter space [11]. In particular, they noted that most of the aerodynamic coefficients in a general model of a parafoil and payload system seem to be strongly correlated, there are many sets of parameters that produce equally good reproductions of the observed flight data, and the additional degrees of freedom accounting for payload motion helped to match the natural eigenvalues of the roll, pitch, and yaw measurements but did not affect the matching of the system trajectory.…”

mentioning

confidence: 99%

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Specialized System Identification for Parafoil and Payload Systems

Ward

Costello

Slegers

2012

Journal of Guidance, Control, and Dynamics

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There are a number of peculiar aspects to parafoil and payload systems that make it difficult to apply conventional system identification procedures used for aerospace systems. Parafoil and payload systems are unique because typically there is very little sensor information available, the sensors that are available are separated from the canopy by a complex network of flexible rigging, the systems are very sensitive to wind and turbulence, the systems exhibit a number of nonlinear behaviors, and the systems exhibit a high degree of variability from flight to flight. The current work describes a robust system identification procedure developed to address the specific difficulties posed by airdrop systems. By employing a two-phase approach that separately considers atmospheric winds estimation and aerodynamic coefficient estimation, a nonlinear, 6-degree-of-freedom dynamic simulation model is generated using only Global Positioning System data from the flight test. The key to this approach is the use of a simplified aerodynamic representation of the canopy, which requires identification of only the steady-state response to control input to completely define the dynamic model. The proposed procedure is demonstrated by creating a simulation model using Global Positioning System data from actual flight tests. To validate the procedure, the dynamic response of the simulation model is then compared to inertial measurement unit data that were not used in any way to develop the simulation model, with excellent results.

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Multicriteria Parametrical Identification of the Parafoil-Load Delivery System

Cited by 17 publications

References 23 publications

Status on Video Data Reduction and Air Delivery Payload Pose Estimation

Status on Video Data Reduction and Air Delivery Payload Pose Estimation

Visualization Tools for Multicriteria Analysis of the Prototype Improvement Problem

Specialized System Identification for Parafoil and Payload Systems

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