3,693 research outputs found
Flexible human-robot cooperation models for assisted shop-floor tasks
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative
robots, i.e., robots able to work alongside and together with humans, could
bring to the whole production process. In this context, an enabling technology
yet unreached is the design of flexible robots able to deal at all levels with
humans' intrinsic variability, which is not only a necessary element for a
comfortable working experience for the person but also a precious capability
for efficiently dealing with unexpected events. In this paper, a sensing,
representation, planning and control architecture for flexible human-robot
cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable
sensors for human action recognition, AND/OR graphs for the representation of
and reasoning upon cooperation models, and a Task Priority framework to
decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier
Coalition based approach for shop floor agility – a multiagent approach
Dissertation submitted for a PhD degree in Electrical Engineering, speciality of Robotics and Integrated Manufacturing from the Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThis thesis addresses the problem of shop floor agility. In order to cope with the disturbances and uncertainties that characterise the current business scenarios faced by manufacturing companies, the
capability of their shop floors needs to be improved quickly, such that these shop floors may be adapted, changed or become easily modifiable (shop floor reengineering).
One of the critical elements in any shop floor reengineering process is the way the control/supervision architecture is changed or modified to accommodate for the new processes and equipment. This thesis,
therefore, proposes an architecture to support the fast adaptation or changes in the control/supervision architecture. This architecture postulates that manufacturing systems are no more than compositions of
modularised manufacturing components whose interactions when aggregated are governed by
contractual mechanisms that favour configuration over reprogramming.
A multiagent based reference architecture called Coalition Based Approach for Shop floor Agility – CoBASA, was created to support fast adaptation and changes of shop floor control architectures with minimal effort. The coalitions are composed of agentified manufacturing components (modules), whose relationships within the coalitions are governed by contracts that are configured whenever a coalition is established. Creating and changing a coalition do not involve programming effort because it only requires changes to the contract that regulates it
A Hierarchical Architecture for Flexible Human-Robot Collaboration
This thesis is devoted to design a software architecture for Human-
Robot Collaboration (HRC), to enhance the robots\u2019 abilities for working
alongside humans. We propose FlexHRC, a hierarchical and
flexible human-robot cooperation architecture specifically designed
to provide collaborative robots with an extended degree of autonomy
when supporting human operators in tasks with high-variability.
Along with FlexHRC, we have introduced novel techniques appropriate
for three interleaved levels, namely perception, representation,
and action, each one aimed at addressing specific traits of humanrobot
cooperation tasks.
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative
robots could bring to the whole production process. In this
context, a yet unreached enabling technology is the design of robots
able to deal at all levels with humans\u2019 intrinsic variability, which is
not only a necessary element to a comfortable working experience
for humans but also a precious capability for efficiently dealing with
unexpected events. Moreover, a flexible assembly of semi-finished
products is one of the expected features of next-generation shop-floor
lines. Currently, such flexibility is placed on the shoulders of human
operators, who are responsible for product variability, and therefore
they are subject to potentially high stress levels and cognitive load
when dealing with complex operations. At the same time, operations
in the shop-floor are still very structured and well-defined. Collaborative
robots have been designed to allow for a transition of such burden
from human operators to robots that are flexible enough to support
them in high-variability tasks while they unfold.
As mentioned before, FlexHRC architecture encompasses three perception,
action, and representation levels. The perception level relies
on wearable sensors for human action recognition and point cloud
data for perceiving the object in the scene. The action level embraces
four components, the robot execution manager for decoupling
action planning from robot motion planning and mapping the symbolic
actions to the robot controller command interface, a task Priority
framework to control the robot, a differential equation solver to
simulate and evaluate the robot behaviour on-the-fly, and finally a
random-based method for the robot path planning. The representation
level depends on AND/OR graphs for the representation of and
the reasoning upon human-robot cooperation models online, a task
manager to plan, adapt, and make decision for the robot behaviors,
and a knowledge base in order to store the cooperation and workspace
information.
We evaluated the FlexHRC functionalities according to the application
desired objectives. This evaluation is accompanied with several
experiments, namely collaborative screwing task, coordinated transportation
of the objects in cluttered environment, collaborative table
assembly task, and object positioning tasks.
The main contributions of this work are: (i) design and implementation
of FlexHRC which enables the functional requirements necessary
for the shop-floor assembly application such as task and team
level flexibility, scalability, adaptability, and safety just a few to name,
(ii) development of the task representation, which integrates a hierarchical
AND/OR graph whose online behaviour is formally specified
using First Order Logic, (iii) an in-the-loop simulation-based decision
making process for the operations of collaborative robots coping with
the variability of human operator actions, (iv) the robot adaptation to
the human on-the-fly decisions and actions via human action recognition,
and (v) the predictable robot behavior to the human user thanks
to the task priority based control frame, the introduced path planner,
and the natural and intuitive communication of the robot with the
human
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
Robots and humans as co-workers? The human-centred perspective of work with autonomous systems
The design of work organisation systems with automated equipment is facing new challenges and the emergence of new concepts. The social aspects that are related with new concepts on the complex work environments (CWE) are becoming more relevant for that design. The work with autonomous systems implies options in the design of workplaces. Especially that happens in such complex environments. The concepts of “agents”, “co-working” or “human-centred technical systems” reveal new dimensions related to human-computer interaction (HCI). With an increase in the number and complexity of those human-technology interfaces, the capacities of human intervention can become limited, originating further problems. The case of robotics is used to exemplify the issues related with automation in working environments and the emergence of new HCI approaches that would include social implications. We conclude that studies on technology assessment of industrial robotics and autonomous agents on manufacturing environment should also focus on the human involvement strategies in organisations. A needed participatory strategy implies a new approach to workplaces design. This means that the research focus must be on the relation between technology and social dimensions not as separate entities, but integrated in the design of an interaction system.With the support of the project Social implications of robotics in manufacturing industry (IR@MI) and project Intuitive interaction between humans and industrial robot systems – a contribution to a conceptual approach (I3RS), both financed by KIT in 2012
On the manipulation of articulated objects in human-robot cooperation scenarios
Articulated and flexible objects constitute a challenge for robot manipulation tasks, but are present in different real-world settings, including home and
industrial environments. Approaches to the manipulation of such objects employ ad hoc strategies to sequence and perform actions on them depending on their physical or geometrical features, and on a priori target object configurations, whereas principled strategies to sequence basic manipulation actions for these objects have not been fully explored in the literature. In this paper, we propose a novel action planning and execution framework for the manipulation of articulated objects, which (i) employs action planning to sequence a set of actions leading to a target articulated object configuration,
and (ii) allows humans to collaboratively carry out the plan with the robot, also interrupting its execution if needed. The framework adopts a formally defined representation of articulated objects. A link exists between the way articulated objects are perceived by the robot, how they are formally represented in the action planning and execution framework, and the complexity of the planning process. Results related to planning performance, and examples with a Baxter dualarm manipulator operating on articulated objects with humans are shown
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