Data validation: a case study for a feed-drive monitoring

The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of requests on control, reliability, availability of machine-tools and on work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring, … The improvement of monitoring of the main machine-tools devices, the feed-drives and the spindles that drive the cutting process, can be realised by estimating their fault sensitive physical parameters from their continuous-time model. We have chosen to use a continuous-time ARX model. We particularly focus on slow time varying phenomena. This estimation should run while there is no machining process to avoid false detection of faults on the machine due to the cutting process. High speed motions, that occur at least for each tool exchange, are exploited. Some functional constraints require the use of an off-line estimation method, we have chosen an ordinary least squares method. Estimating the physical parameters is insufficient to obtain an efficient monitoring. A measurement analysis and validation are necessary as the validation of the estimated physical parameters. An approach of the measurement and physical parameter estimation validation for a NC machine-tool feed-drive is proposed

Sub-optimal Lunar Landing GNC using Non-gimbaled Bio-inspired Optic Flow Sensors

International audienceAutonomous planetary landing is a critical phase in every exploratory space mission. Autopilots have to be safe, reliable, energy-saving, and as light as possible. The 2-D Guidance Navigation and Control (GNC) strategy presented here makes use of biologically inspired landing processes. Based solely on the relative visual motion known as the Optic Flow (OF) assessed with minimalistic 6-pixel 1-D OF sensors and Inertial Measurement Unit measurements, an optimal reference trajectory in terms of the mass was defined for the approach phase. Linear and nonlinear control laws were then implemented in order to track the optimal trajectory. To deal with the demanding weight constraints, a new method of OF estimation was applied, based on a non-gimbaled OF sensor configuration and a linear least squares algorithm. The promising results obtained with Software-In-the-Loop simulations showed that the present full GNC solution combined with our OF bio-inspired sensors is compatible with soft, fuel-efficient lunar spacecraft landing and might also be used as a backup solution in case of conventional sensor failure

Modeling and control of an anthropomorphic robotic hand

Mención Europea en el título de doctorThis thesis presents methods and tools for enabling the successful use of robotic hands. For highly dexterous and/or anthropomorphic robotic hands, these methods have to share some common goals, such as overcoming the potential complexity of the mechanical design and the ability of performing accurate tasks with low and efficient computational cost. A prerequisite for dexterity is to increase the workspace of the robotic hand. For this purpose, the robotic hand must be considered as a single multibody system. Solving the inverse kinematics problem of the whole robotic hand is an arduous task due to the high number of degrees of freedom involved and the possible mechanical limitations, singularities and other possible constraints. The redundancy has proven to be of a great usefulness for dealing with potential constraints. To be able to exploit the redundancy for dealing with constraints, the adopted method for solving the inverse kinematics must be robust and extendable. Obviously, addressing such complex problem, the method will certainly be computationally heavy. Thus, one of the aims of this thesis is to resolve the inverse kinematics problem of the whole robotic hand under constraints, taking into account the computational cost. To this end, this thesis extends and reduces the most recent Selectively Damped Least Squares method which is based on the computation of all singular values, to deal with constraints with a minimum computational cost. New estimation algorithm of singular values and their corresponding singular vectors is proposed to reduce the computational cost. The reduced extended selectively damped least squares method is simulated and experimentally evaluated using an anthropomorphic robotic hand as a test bed. On the other hand, dexterity depends not only on the accuracy of the position control, but also on the exerted forces. The tendon driven modern robotic hands, like the one used in this work, are strongly nonlinear dynamic systems, where motions and forces are transmitted remotely to the finger joints. The problem of modeling and control of position and force simultaneously at low level control is then considered. A new hybrid control structure based on the succession of two sliding mode controllers is proposed. The force is controlled by its own controller which does not need a contact model. The performance of the proposed controller is evaluated by performing the force control directly using the force sensor information of the fingertip, and indirectly using the torque control of the actuator. Finally, we expect that the applications of the methods presented in this thesis can be extended to cover different issues and research fields and in particular they can be used in a variety of algorithm that require the estimation of singular values.This work was partially supported by the European project HANDLE, FP7-231640, and by the Spanish ministry MICINN through FPI scholarship within the project DPI-2005-04302.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Anis Sahbani.- Secretario: Fares Jawad Moh D Abu-Dakka.- Vocal: Claudio Ross

On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs

The purpose of this paper is to study the problem of generalizing the Belavkin-Kalman filter to the case where the classical measurement signal is replaced by a fully quantum non-commutative output signal. We formulate a least mean squares estimation problem that involves a non-commutative system as the filter processing the non-commutative output signal. We solve this estimation problem within the framework of non-commutative probability. Also, we find the necessary and sufficient conditions which make these non-commutative estimators physically realizable. These conditions are restrictive in practice.Comment: 31 page

Robust Camera Location Estimation by Convex Programming

$3$D structure recovery from a collection of $2$D images requires the estimation of the camera locations and orientations, i.e. the camera motion. For large, irregular collections of images, existing methods for the location estimation part, which can be formulated as the inverse problem of estimating $n$ locations $\mathbf{t}_1, \mathbf{t}_2, \ldots, \mathbf{t}_n$ in $\mathbb{R}^3$ from noisy measurements of a subset of the pairwise directions $\frac{\mathbf{t}_i - \mathbf{t}_j}{\|\mathbf{t}_i - \mathbf{t}_j\|}$, are sensitive to outliers in direction measurements. In this paper, we firstly provide a complete characterization of well-posed instances of the location estimation problem, by presenting its relation to the existing theory of parallel rigidity. For robust estimation of camera locations, we introduce a two-step approach, comprised of a pairwise direction estimation method robust to outliers in point correspondences between image pairs, and a convex program to maintain robustness to outlier directions. In the presence of partially corrupted measurements, we empirically demonstrate that our convex formulation can even recover the locations exactly. Lastly, we demonstrate the utility of our formulations through experiments on Internet photo collections.Comment: 10 pages, 6 figures, 3 table