Dynamics Model Abstraction Scheme Using Radial Basis Functions

This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF). Experiments are done using a real robot's arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme

Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.This work has been supported by the EU projects SpikeFORCE (IST-2001-35271), SENSOPAC (IST-028056) and the Spanish National Grant (DPI-2004-07032

Aprendiendo mecatrónica mediante el diseño y construcción de una plataforma de lanzamiento de prototipos de cohetes de agua

Este artículo describe un TFM del Máster en Ciencia de Datos e Ingeniería de Computadores de la Universidad de Granada, consistente en la implementación de una plataforma de lanzamiento de prototipos de cohetes de agua, donde se ha realizado un diseño mecatrónico que permite el control del sistema de forma remota desde el PC. El objetivo que se persigue con este trabajo es mostrar mediante un ejemplo práctico algunos de los conceptos y habilidades adquiridas en dicho Master relacionadas con la mecatrónica. El diseño parte del modelado 3D de cada pieza que interviene en el mecanismo, luego se seleccionan los dispositivos electrónicos que se utilizan para el control de la plataforma, y finalmente se desarrolla el sistema virtual que permite el control desde el PC. El mecanizado de piezas e integración de todos los sistemas se realizó en el laboratorio de robótica y mecatrónica del CITIC-UGR.This paper describes a TFM of the Master in Data Science and Computer Engineering at the University of Granada, which is implementing of a launching pad prototype rocket water, where there has been a mechatronic design that allows control system remotely from the PC. The objective sought to be achieved with this work is to show through a practical example some of the concepts and skills acquired in this Master related to mechatronics. The design of the 3D modeling of each piece involved in the mechanism, then the electronic devices used to control the platform, and finally the virtual system that allows control from the PC develops are selected. Machining parts and integration of all systems was conducted in the laboratory of robotics and mechatronics CITIC-UGR.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadores; Vicerrectorado para la Garantía de la Calidad

Optical Flow in a Smart Sensor Based on Hybrid Analog-Digital Architecture

The purpose of this study is to develop a motion sensor (delivering optical flow estimations) using a platform that includes the sensor itself, focal plane processing resources, and co-processing resources on a general purpose embedded processor. All this is implemented on a single device as a SoC (System-on-a-Chip). Optical flow is the 2-D projection into the camera plane of the 3-D motion information presented at the world scenario. This motion representation is widespread well-known and applied in the science community to solve a wide variety of problems. Most applications based on motion estimation require work in real-time; hence, this restriction must be taken into account. In this paper, we show an efficient approach to estimate the motion velocity vectors with an architecture based on a focal plane processor combined on-chip with a 32 bits NIOS II processor. Our approach relies on the simplification of the original optical flow model and its efficient implementation in a platform that combines an analog (focal-plane) and digital (NIOS II) processor. The system is fully functional and is organized in different stages where the early processing (focal plane) stage is mainly focus to pre-process the input image stream to reduce the computational cost in the post-processing (NIOS II) stage. We present the employed co-design techniques and analyze this novel architecture. We evaluate the system’s performance and accuracy with respect to the different proposed approaches described in the literature. We also discuss the advantages of the proposed approach as well as the degree of efficiency which can be obtained from the focal plane processing capabilities of the system. The final outcome is a low cost smart sensor for optical flow computation with real-time performance and reduced power consumption that can be used for very diverse application domains

Arquitecturas para el procesamiento de sistemas neuronales para el control de robots bioinspirados

Incluye resumen y conclusiones en inglésTesis Univ. Granada. Departamento de Arquitectura y Tecnología de Computadores. Leída el 8 de octubre de 200

Realtime computing platform for spiking neurons (rt-spike),” Neural Networks

Abstract—A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important. Index Terms—Field-programmable gate arrays, pipeline processing, real time system, spiking neural network hardware. I