Mobile Robot Feature-Based SLAM Behavior Learning, and Navigation in Complex Spaces

Learning mobile robot space and navigation behavior, are essential requirements for improved navigation, in addition to gain much understanding about the navigation maps. This chapter presents mobile robots feature-based SLAM behavior learning, and navigation in complex spaces. Mobile intelligence has been based on blending a number of functionaries related to navigation, including learning SLAM map main features. To achieve this, the mobile system was built on diverse levels of intelligence, this includes principle component analysis (PCA), neuro-fuzzy (NF) learning system as a classifier, and fuzzy rule based decision system (FRD)

Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

Introduction: The Third International Conference on Epigenetic Robotics

This paper summarizes the paper and poster contributions to the Third International Workshop on Epigenetic Robotics. The focus of this workshop is on the cross-disciplinary interaction of developmental psychology and robotics. Namely, the general goal in this area is to create robotic models of the psychological development of various behaviors. The term "epigenetic" is used in much the same sense as the term "developmental" and while we could call our topic "developmental robotics", developmental robotics can be seen as having a broader interdisciplinary emphasis. Our focus in this workshop is on the interaction of developmental psychology and robotics and we use the phrase "epigenetic robotics" to capture this focus

Speech Development by Imitation

The Double Cone Model (DCM) is a model of how the brain transforms sensory input to motor commands through successive stages of data compression and expansion. We have tested a subset of the DCM on speech recognition, production and imitation. The experiments show that the DCM is a good candidate for an artificial speech processing system that can develop autonomously. We show that the DCM can learn a repertoire of speech sounds by listening to speech input. It is also able to link the individual elements of speech to sequences that can be recognized or reproduced, thus allowing the system to imitate spoken language

A group-theoretic approach to formalizing bootstrapping problems

The bootstrapping problem consists in designing agents that learn a model of themselves and the world, and utilize it to achieve useful tasks. It is different from other learning problems as the agent starts with uninterpreted observations and commands, and with minimal prior information about the world. In this paper, we give a mathematical formalization of this aspect of the problem. We argue that the vague constraint of having "no prior information" can be recast as a precise algebraic condition on the agent: that its behavior is invariant to particular classes of nuisances on the world, which we show can be well represented by actions of groups (diffeomorphisms, permutations, linear transformations) on observations and commands. We then introduce the class of bilinear gradient dynamics sensors (BGDS) as a candidate for learning generic robotic sensorimotor cascades. We show how framing the problem as rejection of group nuisances allows a compact and modular analysis of typical preprocessing stages, such as learning the topology of the sensors. We demonstrate learning and using such models on real-world range-finder and camera data from publicly available datasets

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

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available