8,485 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN
Collaborative robots are becoming more common on factory floors as well as
regular environments, however, their safety still is not a fully solved issue.
Collision detection does not always perform as expected and collision avoidance
is still an active research area. Collision avoidance works well for fixed
robot-camera setups, however, if they are shifted around, Eye-to-Hand
calibration becomes invalid making it difficult to accurately run many of the
existing collision avoidance algorithms. We approach the problem by presenting
a stand-alone system capable of detecting the robot and estimating its
position, including individual joints, by using a simple 2D colour image as an
input, where no Eye-to-Hand calibration is needed. As an extension of previous
work, a two-stage transfer learning approach is used to re-train a
multi-objective convolutional neural network (CNN) to allow it to be used with
heterogeneous robot arms. Our method is capable of detecting the robot in
real-time and new robot types can be added by having significantly smaller
training datasets compared to the requirements of a fully trained network. We
present data collection approach, the structure of the multi-objective CNN, the
two-stage transfer learning training and test results by using real robots from
Universal Robots, Kuka, and Franka Emika. Eventually, we analyse possible
application areas of our method together with the possible improvements.Comment: 6+n pages, ICRA 2019 submissio
Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities
Research and development work relating to assistive technology
2010-11 (Department of Health)
Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197
The UK landscape for robotics and autonomous systems
Robotics and Autonomous Systems Special Interest Group Report: Innovate UK - Technology Strategy Board
This landscape collates the output from a series of workshops designed to explore the impact on the UK of advances in Robotics and Autonomous Systems (RAS). In overviewing the resulting landscape it is clear that the RAS opportunity, as perceived by the UK community, is extensive and rich and that the UK has the potential to create a strong RAS market.
It is also clear that robotics and autonomous systems will impact on each UK market sector and that the total size of this impact will be significantly greater than the size of the RAS sector itself. Across these sectors strong cross cutting themes exist that can be used to drive synergies to build technical capability and market opportunity.
Within those sectors that will benefit the most from robotics and autonomous systems technology the potential for disruptive innovation and the need to respond to change through the development of new business models is now obvious.
Robotics and autonomous systems do not work in isolation. They will require testing, regulation, standards, innovation, investment and skills together with technical progress and strong collaborative partnerships in order to fully realise the opportunity.
The resulting Landscape carries an essential message; that the UK has a unique opportunity
to engage with robotics and autonomous systems, to exploit existing expertise within the UK and explore its potential, but that other nations are similarly engaged and the UK must now be bold and invest to win.
41 Individuals listed as contributor
Scheduling access to shared space in multi-robot systems
Through this study, we introduce the idea of applying scheduling techniques to allocate spatial resources that are shared among multiple robots moving in a static environment and having temporal constraints on the arrival time to destinations. To illustrate this idea, we present an exemplified algorithm that plans and assigns a motion path to each robot. The considered problem is particularly challenging because: (i) the robots share the same environment and thus the planner must take into account overlapping paths which cannot happen at the same time; (ii) there are time deadlines thus the planner must deal with temporal constraints; (iii) new requests arrive without a priori knowledge thus the planner must be able to add new paths online and adjust old plans; (iv) the robot motion is subject to noise thus the planner must be reactive to adapt to online changes. We showcase the functioning of the proposed algorithm through a set of agent-based simulations
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