127 research outputs found
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
It is true that the "best" neural network is not necessarily the one with the
most "brain-like" behavior. Understanding biological intelligence, however, is
a fundamental goal for several distinct disciplines. Translating our
understanding of intelligence to machines is a fundamental problem in robotics.
Propelled by new advancements in Neuroscience, we developed a spiking neural
network (SNN) that draws from mounting experimental evidence that a number of
individual neurons is associated with spatial navigation. By following the
brain's structure, our model assumes no initial all-to-all connectivity, which
could inhibit its translation to a neuromorphic hardware, and learns an
uncharted territory by mapping its identified components into a limited number
of neural representations, through spike-timing dependent plasticity (STDP). In
our ongoing effort to employ a bioinspired SNN-controlled robot to real-world
spatial mapping applications, we demonstrate here how an SNN may robustly
control an autonomous robot in mapping and exploring an unknown environment,
while compensating for its own intrinsic hardware imperfections, such as
partial or total loss of visual input.Comment: 8 pages, 3 Figures, International Conference on Neuromorphic Systems
(ICONS 2018
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
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Design and Development of an FPGA-based Hardware Accelerator for Corner Feature Extraction and Genetic Algorithm-based SLAM System
Simultaneous Localization and Mapping (SLAM) systems are crucial parts of mobile robots. These systems require a large number of computing units, have significant real-time requirements and are also a vital factor which can determine the stability, operability and power consumption of robots.
This thesis aims to improve the calculation speed of a lidar-based SLAM system in domestic scenes, reduce the power consumption of the SLAM algorithm, and reduce the overall cost of the whole platform. Lightweight, low-power and parallel optimization of SLAM algorithms are researched. In the thesis, two SLAM systems are designed and developed with a focus on
energy-efficient and fast hardware-level design: a geometric method based on corner extraction and a genetic algorithm-based approach. Finally, an FPGA-based hardware accelerated SLAM is implemented and realized, and compared to a software-based system.
As for the front-end SLAM system, a method of using a Corner Feature Extraction (CFE) algorithm on FPGA platforms is first proposed to improve the speed of the feature extraction. Considering building a back-end SLAM system with low power consumption, a SLAM system based on genetic algorithm combined with algorithms such as Extended Kalman Filter (EKF)
and FastSLAM to reduce the amount of calculation in the SLAM system is also proposed. Finally, the thesis also proposes and implements an adaptive feature map which can replace a grid point map to reduce the amount of calculation and utilization of hardware resources.
In this thesis, the lidar SLAM system with front-end and back-end parts mentioned above is implemented on the Xilinx PYNQ Z2 Platform. The implementation is operated on a mobile robot prototype and evaluated in real scenes. Compared with the implementation on the Raspberry Pi 3B+, the implementation in this thesis can save 86.25% of power consumption. The lidar SLAM system only takes 20 ms for location calculation in each scan which is 5.31 times faster compared with the software implementation with EKF
Online Few-shot Gesture Learning on a Neuromorphic Processor
We present the Surrogate-gradient Online Error-triggered Learning (SOEL)
system for online few-shot learningon neuromorphic processors. The SOEL
learning system usesa combination of transfer learning and principles of
computa-tional neuroscience and deep learning. We show that partiallytrained
deep Spiking Neural Networks (SNNs) implemented onneuromorphic hardware can
rapidly adapt online to new classesof data within a domain. SOEL updates
trigger when an erroroccurs, enabling faster learning with fewer updates. Using
gesturerecognition as a case study, we show SOEL can be used for onlinefew-shot
learning of new classes of pre-recorded gesture data andrapid online learning
of new gestures from data streamed livefrom a Dynamic Active-pixel Vision
Sensor to an Intel Loihineuromorphic research processor.Comment: 10 pages, submitted to IEEE JETCAS for revie
Challenges and solutions for autonomous ground robot scene understanding and navigation in unstructured outdoor environments: A review
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges
A high payload aerial platform for infrastructure repair and manufacturing
The use of aerial robots in construction is an area of general interest in the robotics community. Autonomous aerial systems have the potential to improve safety, efficiency and sustainability of industrial construction and repair processes. Several solutions have been deployed in this domain focusing on problems in aerial manipulation and control using existing aerial platforms which are not specialised for the specific challenges in operating on a construction site. This paper presents a new compact, high thrust aerial platform that can act as a modular, application agnostic base for demonstrating a wide variety of capabilities. The platform has been built and tested flying both with manual controls and autonomously in a motion tracking arena while carrying a payload of up to 7.3 kg with a maximum flight time between 10–34 mins (payload dependent). In the future, this platform will be combined with vision based tracking sensors, manipulators and other hardware to operate in and interact with an outdoor environment. Future applications may include manipulation of heavy objects, deposition of material and navigating confined spaces
Analysis of a RGB-D SLAM system using Real-Time Appearance-Based Mapping on Kbot
The Simultaneous Localization And Mapping (SLAM) problem has been a matter of
great importance and research in the area of intelligent robotics. The ability to map the
environment and locate itself on the map simultaneously is an essential tool for mobile
robots in an unknown environment. For localization, it is necessary to have maps. To
map the surroundings, localization is needed. Very much like a chicken-and-egg problem.
SLAM technology solves both the problem of localization as well of mapping together.
Looking for answers to this challenge, different approaches have been developed, i.e.
Visual SLAM (vSLAM), which is SLAM using cameras, in the case of this project, a
RGB-D camera.
In this Bachelor Project, the literature about robot navigation and the state of the art of
SLAM approaches have been reviewed in deep. The system has been setup on the one
hand, in simulation using Gazebo, and on the other hand, in a real a environment sys-
tem; more precisely, using RSAIT’s Kbot in the first floor of the Faculty of Informatics
(UPV/EHU). Experiments in both configurations revealed the potential of the tool for
accurately mapping the environment avoiding odometry error, and allowed to learn the
wide set of visualization tools available to ensure map correction and proper adjustment
of some parameters. The obtained maps have been used later on to command navigation
goals to the robot and to prove the usability of the learned maps
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