51,779 research outputs found
Proceedings of the 1st Standardized Knowledge Representation and Ontologies for Robotics and Automation Workshop
Welcome to IEEE-ORA (Ontologies for Robotics and Automation) IROS workshop. This
is the 1st edition of the workshop on! Standardized Knowledge Representation and
Ontologies for Robotics and Automation. The IEEE-ORA 2014 workshop was held on
the 18th September, 2014 in Chicago, Illinois, USA.
In!the IEEE-ORA IROS workshop, 10 contributions were presented from 7 countries in
North and South America, Asia and Europe. The presentations took place in the
afternoon, from 1:30 PM to 5:00 PM. The first session was dedicated to “Standards for
Knowledge Representation in Robotics”, where presentations were made from the
IEEE working group standards for robotics and automation, and also from the ISO TC
184/SC2/WH7. The second session was dedicated to “Core and Application
Ontologies”, where presentations were made for core robotics ontologies, and also for
industrial and robot assisted surgery ontologies. Three posters were presented in
emergent applications of ontologies in robotics.
We would like to express our thanks to all participants. First of all to the authors,
whose quality work is the essence of this workshop. Next, to all the members of the
international program committee, who helped us with their expertise and valuable
time. We would also like to deeply thank the IEEE-IROS 2014 organizers for hosting
this workshop.
Our deep gratitude goes to the IEEE Robotics and Automation Society, that sponsors!
the IEEE-ORA group activities, and also to the scientific organizations that kindly
agreed to sponsor all the workshop authors work
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
Workshop on disruptive information and communication technologies for innovation and digital transformation
The workshop on Disruptive Information and Communication Technologies for Innovation
and Digital transformation, organized under the scope of the DISRUPTIVE project
(disruptive.usal.es) and held on December 20, 2019 in Bragança, aims to discuss problems,
challenges and benefits of using disruptive digital technologies, namely Internet of Things,
Big data, cloud computing, multi-agent systems, machine learning, virtual and augmented
reality, and collaborative robotics, to support the on-going digital transformation in society.
The main topics included:
• Intelligent Manufacturing Systems
• Industry 4.0 and digital transformation
• Internet of Things
• Cyber-security
• Collaborative and intelligent robotics
• Multi-Agent Systems
• Industrial Cyber-Physical Systems
• Virtualization and digital twins
• Predictive maintenance
• Virtual and augmented reality
• Big Data and advanced data analytics
• Edge and cloud computing
• Digital Transformation
The workshop program included 16 accepted technical papers, 2 invited talks and 1
technical demonstration of use cases.
This volume contains six of the papers presented at the Workshop on Disruptive
Information and Communication Technologies for Innovation and Digital Transformation.info:eu-repo/semantics/publishedVersio
Using Augmented Reality to Assess and Modify Mobile Manipulator Surface Repair Plans
Industrial robotics are redefining inspection and maintenance routines across
multiple sectors, enhancing safety, efficiency, and environmental
sustainability. In outdoor industrial facilities, it is crucial to inspect and
repair complex surfaces affected by corrosion. To address this challenge,
mobile manipulators have been developed to navigate these facilities, identify
corroded areas, and apply protective coatings. However, given that this
technology is still in its infancy and the consequences of improperly coating
essential equipment can be significant, human oversight is necessary to review
the robot's corrosion identification and repair plan. We present a practical
and scalable Augmented Reality (AR)-based system designed to empower
non-experts to visualize, modify, and approve robot-generated surface corrosion
repair plans in real-time. Built upon an AR-based human-robot interaction
framework, Augmented Robot Environment (AugRE), we developed a comprehensive AR
application module called Situational Task Accept and Repair (STAR). STAR
allows users to examine identified corrosion images, point cloud data, and
robot navigation objectives overlaid on the physical environment within these
industrial environments. Users are able to additionally make adjustments to the
robot repair plan in real-time using interactive holographic volumes, excluding
critical nearby equipment that might be at risk of coating overspray. We
demonstrate the entire system using a Microsoft HoloLens 2 and a dual-arm
mobile manipulator. Our future research will focus on evaluating user
experience, system robustness, and real-world validation.Comment: Winning Paper (2nd Prize) at The Second International Horizons of an
Extended Robotics Reality (XR-ROB) Workshop - IEEE IROS 2023 | Workshop
Website: https://sites.google.com/view/xr-robotics-iros2023/home?authuser=
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
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