Mario Luca Fravolini

Machine Vision/GPS Integration Using EKF for the UAV Aerial Refueling Problem

The purpose of this paper is to propose the application of an extended Kalman filter (EKF) for the sensors fusion task within the problem of aerial refueling for unmanned aerial vehicles (UAVs). Specifically, the EKF is used to combine the position data from a global positioning system (GPS) and a machine vision (MV)-based system for providing a reliable estimation of the tanker-UAV relative position throughout the docking and the refueling phase. The performance of the scheme has been evaluated using a virtual environment specifically developed for the study of the UAV aerial refueling problem. Particularly, the EKF-based sensor fusion scheme integrates GPS data with MV-based estimates of the tanker-UAV position derived through a combination of feature extraction, feature classification, and pose estimation algorithms. The achieved results indicate that the accuracy of the relative position using GPS or MV estimates can be improved by at least one order of magnitude with the use of EKF in lieu of other sensor fusion techniques.

Angiotensin converting enzyme deletion allele in different kinds of dementia disorders.

In order to verify the association of Angiotensin converting enzyme (ACE) gene with different kinds of dementia, as well as its association with APO-E (genotype), we performed ACE genotyping in subjects with late-onset probable Alzheimers disease (LOAD, n = 64), early-onset probable Alzheimers disease (EOAD, n = 32), possible Alzheimers disease (pAD, n = 44), vascular dementia (VD, n = 12), age-associated memory impairment (AAMI, n = 15) and 40 healthy age-matched controls, who were previously characterized for APO-E. After the principal component analysis ACE D and Apo-Eepsilon4 alleles disclosed the highest prevalence in the cognitively impaired groups of subjects, Apo-Eepsilon4 being more specific for LOAD and pAD. ACE D allele seems to be an unspecific susceptibility factor for mental decline.

AUTONOMOUS AERIAL REFUELING FOR UAVS USING A COMBINED GPS-MACHINE VISION GUIDANCE

The most important factors affecting the performance of a control scheme for Autonomous Aerial Refueling (AAR) for UAVs are the magnitude of the wake effects from the Tanker and the accuracy of the measurements of the UAV-Tanker distance and attitude leading to the docking. The main objective of the effort described in this paper is the implementation of a detailed modeling and simulation environment for evaluating the AAR problem. In particular, a specific control scheme based on a sensor fusion between GPS- based and Machine Vision-based measurements is proposed. Furthermore, the iterative algorithm used for estimating the position of the optical markers has been modified to be robust to a loss of visibility by one or more optical markers during the docking sequence. The paper presents the results of a detailed analysis of the AAR under different scenarios.

Simulation Environment for Machine Vision Based Aerial Refueling for UAVs

The design of a simulation environment is described for a machine vision (MV)-based approach for the problem of aerial refueling (AR) for unmanned aerial vehicles (UAVs) using the USAF refueling method. MV-based algorithms are implemented within the proposed scheme to detect the relative position and orientation between the UAV and the tanker. Within this effort, techniques and algorithms for the visualization the tanker aircraft in a virtual reality (VR) setting, for the acquisition of the tanker image, for the feature extraction (FE) from the acquired image, for the feature matching (FM) of the features, for the tanker-UAV pose estimation (PE) have been developed and extensively tested in closed-loop simulations. Detailed mathematical models of the tanker and UAV dynamics, refueling boom, turbulence, wind gusts, and tankers wake effects, along with the UAV docking control laws have been implemented within the simulation environment. This paper also presents the results of a study relative to the use of passive markers versus feature extraction for the problem of estimating in real time the UAV-tanker relative position and orientation vectors.

Neural networks-based sensor validation for the flight control system of a B777 research model

Shows the results of the analysis of a scheme for sensor failure, detection, identification and accommodation (SFDIA) using experimental flight data of a research aircraft model. Conventional approaches to the problem are based on observers and Kalman filters while more recent methods are based on neural approximators. The work described in the paper is based on the use of neural networks (NNs) as online learning nonlinear approximators. The performances of two different neural architectures are compared. The first architecture is based on a multi layer perceptron NN trained with the extended backpropagation algorithm. The second architecture is based on a radial basis function (RBF) NN trained with the extended minimal resource allocating networks (EMRAN) algorithms. The experimental data for this study are acquired from the flight-testing of a 1/24th semi-scale B777 research model designed, built, and flown at West Virginia University.

A SIMULATION TOOL FOR ON-LINE REAL TIME PARAMETER IDENTIFICATION

This paper describes a simulation tool developed at West Virginia University (WVU) for online aircraft parameter identification (PID) within a specific fault tolerant flight control scheme for the NASA IFCS F-15 program. The simulation package developed by WVU researchers is modular and flexible so that different methods and/or approaches can be used for each of the tasks of the general fault tolerant control system, such as aircraft model, controller, parameter identification method, and on-line data storage. Numerous simulation options are directly available to the user through specific graphical user interface. These options allow to select among different control loop configurations, different versions of the parameter identification method, and different failure scenarios.

Design of a Neural Network Adaptive Controller via a Constrained Invariant Ellipsoids Technique

In safety critical applications, control architectures based on adaptive neural networks (NNs) must satisfy strict design specifications. This paper presents a practical approach for designing a mixed linear/adaptive model reference controller that recovers the performance of a reference model, and guarantees the boundedness of the tracking error within an a priori specified compact domain, in the presence of bounded uncertainties. The linear part of the controller results from the solution of an optimization problem where specifications are expressed as linear matrix inequality constraints. The linear controller is then augmented with a general adaptive NN that compensates for the uncertainties. The only requirement for the NN is that its output must be confined within pre-specified saturation limits. Toward this end a specific NN output confinement algorithm is proposed in this paper. The main advantages of the proposed approach are that requirements in terms of worst-case performance can be easily defined during the design phase, and that the design of the adaptation mechanism is largely independent from the synthesis of the linear controller. A numerical example is used to illustrate the design methodology.

Comparing Optical Flow Algorithms Using 6-DOF Motion of Real-World Rigid Objects

The application of optical flow algorithms to guidance and navigation problems has gained considerable interest in recent years. This paper summarizes the results of a comparative study on the accuracy of nine different optical flow (OF) algorithms using videos that are captured from an on-board camera during the flight of an autonomous aircraft model. The comparison among the algorithms relies on two formulas that are used both to calculate the ideal OF generated by the motion of a rigid body in the camera field of view and to estimate the linear and angular velocity from the OF.

Addressing corner detection issues for machine vision based UAV aerial refueling

This paper describes the results of the analysis of specific ‘corner detection’ algorithms within a Machine Vision approach for the problem of aerial refueling for unmanned aerial vehicles. Specifically, the performances of the SUSAN and the Harris corner detection algorithms have been compared. A critical goal of this study was to evaluate the interface of these feature extraction schemes with the successive detection and labeling, and pose estimation schemes in the overall scheme. Closed-loop simulations were performed using a Simulink®-based simulation environment to reproduce docking maneuvers using the US Air Force refueling boom.

Comparison of point matching algorithms for the UAV aerial refueling problem

This paper describes the results of a study focused on the evaluation of the performance of specific algorithms for the “point matching” task within the general problem of applying machine vision-based control laws for the problem of aerial refueling (AR) for UAVs. Two different point matching algorithms for the identification of the corner-points of the tanker are proposed. A detailed study of the algorithms is performed with special emphasis on the correct matching and required computational effort. The results show the importance of a correct point-matching scheme for obtaining accurate pose estimation; furthermore, the analysis highlights the tradeoffs involved in the selection of the appropriate algorithms.