Herman Bruyninckx

Bio: Herman Bruyninckx is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Robot & Kalman filter. The author has an hindex of 42, co-authored 304 publications receiving 7296 citations. Previous affiliations of Herman Bruyninckx include Bonn-Rhein-Sieg University of Applied Sciences & Augsburg College.

Open robot control software: the OROCOS project

Abstract: This paper introduces the OROCOS project that aims at becoming a general-purpose and open robot control software package. OROCOS follows the open source development model that has been proven to work in many other general-purpose software packages, such as Linux, Apache, Perl or LATEX. The paper focuses on the long-term vision of this start-up project, motivates which strategic and innovative design decisions are to be taken (a CORBA-like component architecture being the most important one), and lists other projects on which OROCOS could build. The success of OROCOS depends critically on how many researchers and engineers can be motivated to contribute code, documentation and feedback to the project.

Modeling and Control of Complex Physical Systems: The Port-Hamiltonian Approach

Abstract: Energy exchange is a major foundation of the dynamics of physical systems, and, hence, in the study of complex multi-domain systems, methodologies that explicitly describe the topology of energy exchanges are instrumental in structuring the modeling and the computation of the system's dynamics and its control. This book is the outcome of the European Project "Geoplex" (FP5 IST-2001-34166) that studied and extended such system modeling and control methodologies. This unique book starts from the basic concept of port-based modeling, and extends it to port-Hamiltonian systems. This generic paradigm is applied to various physical domains, showing its power and unifying flexibility for real multi-domain systems.

European Robotics Symposium 2008

Abstract: Adaptive Multiple Resources Consumption Control for an Autonomous Rover.- Adaptive Snake Robot Locomotion: A Benchmarking Facility for Experiments.- Architecture for Neuronal Cell Control of a Mobile Robot.- The Ares Robot: Case Study of an Affordable Service Robot.- Balancing the Information Gain Against the Movement Cost for Multi-robot Frontier Exploration.- Compiling POMDP Models for a Multimodal Service Robot from Background Knowledge.- Constraint Based Object State Modeling.- A COTS-Based Mini Unmanned Aerial Vehicle (SR-H3) for Security, Environmental Monitoring and Surveillance Operations: Design and Test.- Eyes-Neck Coordination Using Chaos.- Formation Graphs and Decentralized Formation Control of Multi Vehicles with Kinematics Constraints.- Global Urban Localization of an Outdoor Mobile Robot with Genetic Algorithms.- Grip Force Control Using Vision-Based Tactile Sensor for Dexterous Handling.- HNG: A Robust Architecture for Mobile Robots Systems.- Information Relative Map Going Toward Constant Time SLAM.- Measuring Motion Expressiveness in Wheeled Mobile Robots.- Modeling, Simulation and Control of Pneumatic Jumping Robot.- Multilayer Perceptron Adaptive Dynamic Control of Mobile Robots: Experimental Validation.- Path Planning and Tracking Control for an Automatic Parking Assist System.- Performance Evaluation of Ultrasonic Arc Map Processing Techniques by Active Snake Contours.- Planning Robust Landmarks for Sensor Based Motion.- Postural Control on a Quadruped Robot Using Lateral Tilt: A Dynamical System Approach.- Propose of a Benchmark for Pole Climbing Robots.- Rat's Life: A Cognitive Robotics Benchmark.- Reactive Trajectory Deformation to Navigate Dynamic Environments.- Recovery in Autonomous Robot Swarms.- Robot Force/Position Tracking on a Surface of Unknown Orientation.- Scalable Operators for Feature Extraction on 3-D Data.- Semi-autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization.- A Simple Visual Navigation System with Convergence Property.- Stability of On-Line and On-Board Evolving of Adaptive Collective Behavior.- A Unified Framework for Whole-Body Humanoid Robot Control with Multiple Constraints and Contacts.- Visual Approaches for Handle Recognition.- Visual Top-Down Attention Framework for Robots in Dynamic Environments.- Visual Topological Mapping.- 3D Mapping and Localization Using Leveled Map Accelerated ICP.

Kalman filters for non-linear systems: a comparison of performance

Abstract: The Kalman filter is a well-known recursive state estimator for linear systems. In practice, the algorithm is often used for non-linear systems by linearizing the system's process and measurement models. Different ways of linearizing the models lead to different filters. In some applications, these ‘Kalman filter variants’ seem to perform well, while for other applications they are useless. When choosing a filter for a new application, the literature gives us little to rely on. This paper tries to bridge the gap between the theoretical derivation of a Kalman filter variant and its performance in practice when applied to a non-linear system, by providing an application-independent analysis of the performances of the common Kalman filter variants. Correlated uncertainties can be dealt with by augmenting the state vector, this is the original formulation of the KF (Kalman 1960). Expressed in this new state vector, the process and measurement models are of the form (1) and (2) with uncorrelated uncertainties...

Comment on "A new method for the nonlinear transformation of means and covariances in filters and estimators" [with authors' reply]

Abstract: The above paper (Julier et al. IEEE Trans. Automat. Contr, vol. 45, pp. 477-82, 2000) generalizes the Kalman filter to nonlinear systems by transforming approximations of the probability distributions through the nonlinear process and measurement functions. This comment derives exactly the same estimator by linearizing the process and measurement functions by a statistical linear regression through some regression points (in contrast with the extended Kalman filter which uses an analytic linearization in one point). This insight allows one: 1) to understand/predict the performance of the estimator for specific applications, and 2) to make adaptations to the estimator (i.e., the choice of the regression points and their weights) in those cases where the original formulation does not assure good results. In reply the authors state that the commenters conclusion is unnecessarily narrow interpretation of results.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Unscented filtering and nonlinear estimation

Abstract: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.

Reliability and validity

Abstract: Definition: To what extent does the study allow us to draw conclusions about a causal effect between two or more constructs? Issues: Selection, maturation, history, mortality, testing, regression towrd the mean, selection by maturation, treatment by mortality, treatment by testing, measured treatment variables Increase: Eliminate the threats, above all do experimental manipulations, random assignment, and counterbalancing.