23 research outputs found
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
Online Non-Collocated Estimation of Payload and Articular Stress for Real-Time Human Ergonomy Assessment
Improving the quality of work for human beings is receiving a lot of attention from multiple research communities. In particular, digital transformation in human factors and ergonomics is going to empower the next generation of the socio-technical workforce. The use of wearable sensors, collaborative robots, and exoskeletons, coupled with novel technologies for the real-time assessment of human ergonomy forms the crux of this digital transformation. In this direction, this paper focuses on the open problem of estimating the interaction wrench experienced at the human extremities (such as hands), where the feasibility of direct sensor measurements is not practical. We refer to our approach as non-collocated wrench estimation, as we aim to estimate the wrench at known contact locations but without using any direct force-torque sensor measurements at these known locations. We achieve this by extending the formulation of stochastic inverse dynamics for humans by considering a centroidal dynamics constraint to perform a reliable non-collocated estimation of interaction wrench and the joint torques (articular stress) experienced as a direct consequence of the interaction. Our approach of non-collocated estimation is thoroughly validated in terms of payload estimation and articular stress estimation through validation and experimental scenarios involving dynamic human motions like walking