12,060 research outputs found
Towards a Shared Control Navigation Function:Efficiency Based Command Modulation
This paper presents a novel shared control algorithm for robotized
wheelchairs. The proposed algorithm is a new method to extend
autonomous navigation techniques into the shared control domain. It reactively
combines user’s and robot’s commands into a continuous function
that approximates a classic Navigation Function (NF) by weighting input
commands with NF constraints. Our approach overcomes the main drawbacks
of NFs -calculus complexity and limitations on environment
modeling- so it can be used in dynamic unstructured environments. It also
benefits from NF properties: convergence to destination, smooth paths
and safe navigation. Due to the user’s contribution to control, our function
is not strictly a NF, so we call it a pseudo-navigation function (PNF)
instead.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Robot Navigation in Unseen Spaces using an Abstract Map
Human navigation in built environments depends on symbolic spatial
information which has unrealised potential to enhance robot navigation
capabilities. Information sources such as labels, signs, maps, planners, spoken
directions, and navigational gestures communicate a wealth of spatial
information to the navigators of built environments; a wealth of information
that robots typically ignore. We present a robot navigation system that uses
the same symbolic spatial information employed by humans to purposefully
navigate in unseen built environments with a level of performance comparable to
humans. The navigation system uses a novel data structure called the abstract
map to imagine malleable spatial models for unseen spaces from spatial symbols.
Sensorimotor perceptions from a robot are then employed to provide purposeful
navigation to symbolic goal locations in the unseen environment. We show how a
dynamic system can be used to create malleable spatial models for the abstract
map, and provide an open source implementation to encourage future work in the
area of symbolic navigation. Symbolic navigation performance of humans and a
robot is evaluated in a real-world built environment. The paper concludes with
a qualitative analysis of human navigation strategies, providing further
insights into how the symbolic navigation capabilities of robots in unseen
built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and
Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see
https://btalb.github.io/abstract_map/ for access to softwar
A Framework for Interactive Teaching of Virtual Borders to Mobile Robots
The increasing number of robots in home environments leads to an emerging
coexistence between humans and robots. Robots undertake common tasks and
support the residents in their everyday life. People appreciate the presence of
robots in their environment as long as they keep the control over them. One
important aspect is the control of a robot's workspace. Therefore, we introduce
virtual borders to precisely and flexibly define the workspace of mobile
robots. First, we propose a novel framework that allows a person to
interactively restrict a mobile robot's workspace. To show the validity of this
framework, a concrete implementation based on visual markers is implemented.
Afterwards, the mobile robot is capable of performing its tasks while
respecting the new virtual borders. The approach is accurate, flexible and less
time consuming than explicit robot programming. Hence, even non-experts are
able to teach virtual borders to their robots which is especially interesting
in domains like vacuuming or service robots in home environments.Comment: 7 pages, 6 figure
A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction
Picking up objects requested by a human user is a common task in human-robot
interaction. When multiple objects match the user's verbal description, the
robot needs to clarify which object the user is referring to before executing
the action. Previous research has focused on perceiving user's multimodal
behaviour to complement verbal commands or minimising the number of follow up
questions to reduce task time. In this paper, we propose a system for reference
disambiguation based on visualisation and compare three methods to disambiguate
natural language instructions. In a controlled experiment with a YuMi robot, we
investigated real-time augmentations of the workspace in three conditions --
mixed reality, augmented reality, and a monitor as the baseline -- using
objective measures such as time and accuracy, and subjective measures like
engagement, immersion, and display interference. Significant differences were
found in accuracy and engagement between the conditions, but no differences
were found in task time. Despite the higher error rates in the mixed reality
condition, participants found that modality more engaging than the other two,
but overall showed preference for the augmented reality condition over the
monitor and mixed reality conditions
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