259 research outputs found
Furthering Service 4.0: Harnessing Intelligent Immersive Environments and Systems
With the increasing complexity of service operations in different industries and more advanced uses of specialized equipment and procedures, the great current challenge for companies is to increase employees' expertise and their ability to maintain and improve service quality. In this regard, Service 4.0 aims to support and promote innovation in service operations using emergent technology. Current technological innovations present a significant opportunity to provide on-site, real-time support for field service professionals in many areas
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Real-time robotic tasks for cyber-physical avatars
Although modern robots can perform complex tasks using sophisticated algorithms that are specialized to a particular task and environment, creating robots capable of completing tasks in unstructured environments without human guidance (e.g., through teleoperation) remains a challenge. In this research, we present a framework to meet this challenge for a "cyberphysical avatar," which is defined to be a semi-autonomous robotic system that adjusts to an unstructured environment and performs physical tasks subject to critical timing constraints while under human supervision. This thesis first realizes a cyberphysical avatar that integrates three key technologies: (1) whole body-compliant control, (2) skill acquisition from machine learning (neuroevolution methods and deep learning), and (3) vision-based control through visual servoing. Body-compliant control is essential for operator safety because avatars perform cooperative tasks in close proximity to humans; machine learning enables "programming" avatars such that they can be used by non-experts for a large array of tasks, some unforeseen, in an unstructured environment; the visual servoing technique is indispensable for facilitating feedback control in human avatar interaction. This thesis proposes and demonstrates a systematically incremental approach to automating robotic tasks by decomposing a non-trivial task into stages, each of which may be automated by integrating the aforementioned techniques. We design and implement the controllers for two semi-autonomous robots that integrate three key techniques for grasping and pick-and-place tasks. While a general theory is beyond reach, we present a study on the tradeoffs between three design metrics for robotic task systems: (1) the amount of training effort for the robots to perform the task, (2) the time available to complete the task when the command is given, and (3) the quality of the result of the performed task. The tradeoff study in this design space uses the imprecise computation model as a framework to evaluate specific types of tasks: (1) grasping an unknown object and (2) placing the object in a target position. We demonstrate the generality of our integration methodology by applying it to two different robots, Dreamer and Hoppy. Our approach is evaluated by the performance of the robots in trading off between task completion time, training time and task completion success rate, in an environment similar to those in the recent Amazon Picking Challenge.Computer Science
Human-Robot Perception in Industrial Environments: A Survey
Perception capability assumes significant importance for human–robot interaction. The
forthcoming industrial environments will require a high level of automation to be flexible and
adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous
and collaborative robots able to adapt to varying and dynamic conditions of the environment,
including the presence of human beings, will have an ever-greater role in this context. However, if
the robot is not aware of the human position and intention, a shared workspace between robots and
humans may decrease productivity and lead to human safety issues. This paper presents a survey on
sensory equipment useful for human detection and action recognition in industrial environments.
An overview of different sensors and perception techniques is presented. Various types of robotic
systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile
robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to
perceive and react to the presence of human operators in industrial cooperative and collaborative
applications. The paper also introduces two proofs of concept, developed by the authors for future
collaborative robotic applications that benefit from enhanced capabilities of human perception and
interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human
safety in tasks requiring human collision avoidance or moving obstacles detection. The second
one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing
assigned tasks within an industrial space shared with human operators
Assistive Technology to Improve Collaboration in Children with ASD: State-of-the-Art and Future Challenges in the Smart Products Sector
Within the field of products for autism spectrum disorder, one of the main research areas is focused on the development of assistive technology. Mid and high-tech products integrate interactive and smart functions with multisensory reinforcements, making the user experience more intuitive, adaptable, and dynamic. These products have a very significant impact on improving the skills of children with autism, including collaboration and social skills, which are essential for the integration of these children into society and, therefore, their well-being. This work carried out an exhaustive analysis of the scientific literature, as well as market research and trends, and patent analysis to explore the state-of-the-art of assistive technology and smart products for children with ASD, specifically those aimed at improving social and communication skills. The results show a reduced availability of products that act as facilitators of the special needs of children with ASD, which is even more evident for products aimed at improving collaboration skills. Products that allow the participation of several users simultaneously through multi-user interfaces are required. On top of this, the trend toward virtual environments is leading to a loss of material aspects in the design that are essential for the development of these children
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Thanks to the augmented convenience, safety advantages, and potential
commercial value, Intelligent vehicles (IVs) have attracted wide attention
throughout the world. Although a few autonomous driving unicorns assert that
IVs will be commercially deployable by 2025, their implementation is still
restricted to small-scale validation due to various issues, among which precise
computation of control commands or trajectories by planning methods remains a
prerequisite for IVs. This paper aims to review state-of-the-art planning
methods, including pipeline planning and end-to-end planning methods. In terms
of pipeline methods, a survey of selecting algorithms is provided along with a
discussion of the expansion and optimization mechanisms, whereas in end-to-end
methods, the training approaches and verification scenarios of driving tasks
are points of concern. Experimental platforms are reviewed to facilitate
readers in selecting suitable training and validation methods. Finally, the
current challenges and future directions are discussed. The side-by-side
comparison presented in this survey not only helps to gain insights into the
strengths and limitations of the reviewed methods but also assists with
system-level design choices.Comment: 20 pages, 14 figures and 5 table
Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey
Abstract
There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels
A modern vehicle fitted with sensors, actuators, and Electronic Control Units
(ECUs) can be divided into several operational subsystems called Functional
Working Groups (FWGs). Examples of these FWGs include the engine system,
transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels
that gauge vehicular operating conditions. This data rich environment is
conducive to the development of Predictive Maintenance (PdM) technologies.
Undercutting various PdM technologies is the need for robust anomaly detection
models that can identify events or observations which deviate significantly
from the majority of the data and do not conform to a well defined notion of
normal vehicular operational behavior. In this paper, we introduce the Vehicle
Performance, Reliability, and Operations (VePRO) dataset and use it to create a
multi-phased approach to anomaly detection. Utilizing Temporal Convolution
Networks (TCN), our anomaly detection system can achieve 96% detection accuracy
and accurately predicts 91% of true anomalies. The performance of our anomaly
detection system improves when sensor channels from multiple FWGs are utilized
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