Acquisition and Interpretation of 3-D Sensor Data from Touch

Acquisition of 3-D scene information has focused on either passive 2-D imaging methods (stereopsis, structure from motion etc.) or 3-D range sensing methods (structured lighting, laser scanning etc.). Little work has been done in using active touch sensing with a multi-fingered robotic hand to acquire scene descriptions, even though it is a well developed human capability. Touch sensing differs from other more passive sensing modalities such as vision in a number of ways. A multi-fingered robotic hand with touch sensors can probe, move, and change its environment. This imposes a level of control on the sensing that makes it typically more difficult than traditional passive sensors in which active control is not an issue. Secondly, touch sensing generates far less data than vision methods; this is especially intriguing in light of psychological evidence that shows humans can recover shape and a number of other object attributes very reliably using touch alone. Future robotic systems will need to use dextrous robotic hands for tasks such as grasping, manipulation, assembly, inspection and object recognition. This paper describes our use of touch sensing as part of a larger system we are building for 3-D shape recovery and object recognition using touch and vision methods. It focuses on three exploratory procedures we have built to acquire and interpret sparse 3-D touch data: grasping by containment, planar surface exploration and surface contour exploration. Experimental results for each of these procedures are presented

GUI system for Elders/Patients in Intensive Care

In the old age, few people need special care if they are suffering from specific diseases as they can get stroke while they are in normal life routine. Also patients of any age, who are not able to walk, need to be taken care of personally but for this, either they have to be in hospital or someone like nurse should be with them for better care. This is costly in terms of money and man power. A person is needed for 24x7 care of these people. To help in this aspect we purposes a vision based system which will take input from the patient and will provide information to the specified person, who is currently may not in the patient room. This will reduce the need of man power, also a continuous monitoring would not be needed. The system is using MS Kinect for gesture detection for better accuracy and this system can be installed at home or hospital easily. The system provides GUI for simple usage and gives visual and audio feedback to user. This system work on natural hand interaction and need no training before using and also no need to wear any glove or color strip.Comment: In proceedings of the 4th IEEE International Conference on International Technology Management Conference, Chicago, IL USA, 12-15 June, 201

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Collaborative robot control with hand gestures

Mestrado de dupla diplomação com a Université Libre de TunisThis thesis focuses on hand gesture recognition by proposing an architecture to control a collaborative robot in real-time vision based on hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bar e hand in a cluttered background using skin detection and contour comparison. The second stage allows recognizing hand gestures using a Machine learning method algorithm. Finally an interface has been developed to control the robot over. Our hand gesture recognition system consists of two parts, in the first part for every frame captured from a camera we extract the keypoints for every training image using a machine learning algorithm, and we appoint the keypoints from every image into a keypoint map. This map is treated as an input for our processing algorithm which uses several methods to recognize the fingers in each hand. In the second part, we use a 3D camera with Infrared capabilities to get a 3D model of the hand to implement it in our system, after that we track the fingers in each hand and recognize them which made it possible to count the extended fingers and to distinguish each finger pattern. An interface to control the robot has been made that utilizes the previous steps that gives a real-time process and a dynamic 3D representation.Esta dissertação trata do reconhecimento de gestos realizados com a mão humana, propondo uma arquitetura para interagir com um robô colaborativo, baseado em visão computacional, rastreamento e reconhecimento de gestos. O primeiro estágio do sistema desenvolvido permite detectar e rastrear a presença de uma mão em um fundo desordenado usando detecção de pele e comparação de contornos. A segunda fase permite reconhecer os gestos das mãos usando um algoritmo do método de aprendizado de máquina. Finalmente, uma interface foi desenvolvida para interagir com robô. O sistema de reconhecimento de gestos manuais está dividido em duas partes. Na primeira parte, para cada quadro capturado de uma câmera, foi extraído os pontos-chave de cada imagem de treinamento usando um algoritmo de aprendizado de máquina e nomeamos os pontos-chave de cada imagem em um mapa de pontos-chave. Este mapa é tratado como uma entrada para o algoritmo de processamento que usa vários métodos para reconhecer os dedos em cada mão. Na segunda parte, foi utilizado uma câmera 3D com recursos de infravermelho para obter um modelo 3D da mão para implementá-lo em no sistema desenvolvido, e então, foi realizado os rastreio dos dedos de cada mão seguido pelo reconhecimento que possibilitou contabilizar os dedos estendidos e para distinguir cada padrão de dedo. Foi elaborado uma interface para interagir com o robô manipulador que utiliza as etapas anteriores que fornece um processo em tempo real e uma representação 3D dinâmica

Haptic object recognition using a multi-fingered dextrous hand

The use of a dextrous, multifingered hand for high-level object recognition tasks is considered. The paradigm is model-based recognition in which the objects are modeled and recovered as superquadratics, which are shown to have a number of important attributes that make them well suited for such a task. Experiments have been performed to recover the shape of objects using sparse contacts point data from the hand with promising results. The authors also propose an approach to using tactile data in conjunction with the dextrous hand to build a library of grasping and exploration primitives that can be used in recognizing and grasping more complex multipart objects

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose