A new vertex result for robustness problems with interval matrix uncertainty

This paper¹ addresses a family of robustness problems in which the system under consideration is affected by interval matrix uncertainty. The main contribution of the paper is a new vertex result that drastically reduces the number of extreme realizations required to check robust feasibility. This vertex result allows one to solve, in a deterministic way and without introducing conservatism, the corresponding robustness problem for small and medium size problems. For example, consider quadratic stability of an autonomous n_x dimensional system. In this case, instead of checking vertices, we show that it suffices to check 2^2n_x specially constructed systems. This solution is still exponential, but this is not surprising because the problem is NP-hard. Finally, vertex extensions to multiaffine interval families and some sufficient conditions (in LMI form) for robust feasibility are presented. Some illustrative examples are also given.     

Motion Planning for Haptic Guidance

Haptic devices allow a user to feel either reaction forces from virtual interactions or reaction forces reflected from a remote site during a bilateral teleoperation task. Also, guiding forces can be exerted to train the user in the performance of a virtual task or to assist him/her to safely teleoperate a robot. The generation of guiding forces relies on the existence of a motion plan that provides the direction to be followed to reach the goal from any free configuration of the configuration space (-space). This paper proposes a method to obtain such a plan that interleaves a sampling-based exploration of -space with an efficient computation of harmonic functions. A deterministic sampling sequence (with a bias based on harmonic function values) is used to obtain a hierarchical cell decomposition model of -space. A harmonic function is iteratively computed over the partially known model using a novel approach. The harmonic function is the navigation function used as motion plan. The approach has been implemented in a planner (called the Kautham planner) that, given an initial and a goal configuration, provides: (a) a channel of cells connecting the cell that contains the initial configuration with the cell that contains the goal configuration; (b) two harmonic functions over the whole -space, one that guides motions towards the channel and another that guides motions within the channel towards the goal; and (c) a path computed over a roadmap built with the free samples of the channel. The harmonic functions and the solution path are then used to generate the guiding forces for the haptic device. The planning approach is illustrated with examples on 2D and 3D workspaces. This work was partially supported by the CICYT projects DPI2005-00112 and DPI2007-63665.     

Beacon-Less Geographic Routing in Real Wireless Sensor Networks

Geographic Routing(GR)algorithms require nodes to periodically transmit HELLO messages to allow neigh- bors to know their positions(beaconing mechanism).Beacon-less routing algorithms have recently been proposed to reduce the control overheads due to these messages.However,existing beacon-less algorithms have not considered realistic physical layers.Therefore,those algorithms cannot work properly in realistic scenarios.In this paper we present a new beacon- less routing protocol called BOSS.Its design is based on the conclusions of our open-field experiments using Tmote-sky sensors.BOSS is adapted to error-prone networks and incorporates a new mechanism to reduce collisions and duplicate messages produced during the selection of the next forwarder node.We compare BOSS with Beacon-Less Routing(BLR) and Contention-Based Forwarding(CBF)algorithms through extensive simulations.The results show that our scheme is able to achieve almost perfect packet delivery ratio(like BLR)while having a low bandwidth consumption(even lower than CBF).Additionally,we carried out an empirical evaluation in a real testbed that shows the correctness of our simulation results.     

GLSV: Graphics library stereo vision for OpenGL

This work proposes the development of an auxiliary library for use with OpenGL, to facilitate the creation of graphic applications incorporating stereoscopic representation. This library, christened graphics library stereo vision (GLSV), is designed to remove all calculations involving knowledge of stereo vision theory from the task performed by the programmer without the latter having to change the way he/she has been working with the OpenGL library. The GLSV is distributed under the terms of the GNU Library General Public License agreement.     

An artificial life approach to dense stereo disparity

This article presents an adaptive approach to improving the infection algorithm that we have used to solve the dense stereo matching problem. The algorithm presented here incorporates two different epidemic automata along a single execution of the infection algorithm. The new algorithm attempts to provide a general behavior of guessing the best correspondence between a pair of images. Our aim is to provide a new strategy inspired by evolutionary computation, which combines the behaviors of both automata into a single correspondence problem. The new algorithm will decide which automata will be used based on the transmission of information and mutation, as well as the attributes, texture, and geometry, of the input images. This article gives details about how the rules used in the infection algorithm are coded. Finally, we show experiments with a real stereo pair, as well as with a standard test bed, to show how the infection algorithm works.     

All-optical implementation of a self-organizing map: Learning and taxonomy capability assessment

We present some experimental results obtained with an all-optical implementation of a self-organizing map. The system is based on two ferroelectric liquid-crystal, bistable optically addressed spatial light modulators (BOASLM's) used as parallel read/write optical memories and as thresholding devices. Modifications made to adapt the basic algorithm to the BOASLM's are studied in computer simulations that predict training-class loss due to the simplified decision stage as the principal drawback. Experimental results show good system behavior in recognition mode, demonstrating generalization properties and confirming the choice of a BOASLM memory. All-optical learning is shown in simple cases, and the elementary BOASLM decision function is confirmed as the major limiting factor. The ultimate possible system performance is discussed, and ways of overcoming present limits are suggested.     

Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

International Journal of Control, Automation and Systems - This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle...     

Experience report on the application of genetic algorithms to reduce costs of the software validation process in the automotive sector during an engine control unit project

Software Quality Journal - The number of electronic control units (ECU) installed in vehicles is increasingly high. Manufacturers must improve the software quality and reduce cost by proposing...     

Bond calibration method for Young’s modulus determination in the discrete element method framework

A new methodology for the calibration of bond microparameters in rocks represented by a package of joined random spherical particles in the discrete element method (DEM) framework is presented. Typically, calibration is achieved through a trial-and-error procedure using several DEM simulations of uniaxial compressive tests (UCTs). The bond calibration model (BCM) does not need a time-dependent UCT-DEM simulation to establish the relation between the microproperties of the bond and the macroproperties of the rock specimen. The BCM uses matrices to describe the interaction forces exerted by bonds and, by means of an assembly process similar to the finite element method, it can describe the complex network of bonds, enabling the model to capture small variations in particle size and bond distribution as demonstrated in this work. In this work, the BCM is presented and compared with UCT simulations performed using Esys Particle software. Multiple simulations are done with constant bond properties and different particle size ratios (\(D_{MAX}/D_{MIN})\) that cause small variations in the specimen’s Young’s modulus; these variations are well captured by the BCM with an error of <10%.