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Mechatronics consist of the integration of mechanical
engineering, electronic integration and computer science/
engineering. These broad fields are essential for robotic
systems, yet it makes it difficult for the researchers to specialize
and be experts in all these fields. Collaboration between
researchers allow for the integration of experience and specialization,
to allow optimized systems. Collaboration between the
European countries and South Africa is critical, as each country
has different resources available, which the other countries
might not have. Applications with the need for approval of
any restrictions, can also be obtained easier in some countries
compared to others, thus preventing the delays of research.
Some problems that have been experienced are discussed, with
the Robotics Center of South Africa as a possible solution.
With autonomous mobile robots receiving increased
attention in industrial contexts, the need for benchmarks
becomes more and more an urgent matter. The RoboCup
Logistics League (RCLL) is one specific industry-inspired scenario
focusing on production logistics within a Smart Factory.
In this paper, we describe how the RCLL allows to assess the
performance of a group of robots within the scenario as a
whole, focusing specifically on the coordination and cooperation
strategies and the methods and components to achieve them.
We report on recent efforts to analyze performance of teams in
2014 to understand the implications of the current grading
scheme, and derived criteria and metrics for performance
assessment based on Key Performance Indicators (KPI) adapted
from classic factory evaluation. We reflect on differences and
compatibility towards RoCKIn, a recent major benchmarking
European project.
Ground or aerial robots equipped with advanced sensing technologies, such as three-dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state-of-the-art and off-the-shelf components and algorithms are suited for reconnaissance in current disaster-relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster-relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state-of-the-art and off-the-shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.
The main objective of our ROS Summer School series is to introduce MA level students to program mobile robots with the Robot Operating System (ROS). ROS is a robot middleware that is used my many research institutions world-wide. Therefore, many state-of-the-art algorithms of mobile robotics are available in ROS and can be deployed very easily. As a basic robot platform we deploy a 1/10 RC cart that is wquipped with an Arduino micro-controller to control the servo motors, and an embedded PC that runs ROS. In two weeks, participants get to learn the basics of mobile robotics hands-on. We describe our teaching concepts and our curriculum and report on the learning success of our students.
We present a robotic tool that autonomously follows a conversation to enable remote presence in video conferencing. When humans participate in a meeting with the help of video conferencing tools, it is crucial that they are able to follow the conversation both with acoustic and visual input. To this end, we design and implement a video conferencing tool robot that uses binaural sound source localization as its main source to autonomously orient towards the currently talking speaker. To increase robustness of the acoustic cue against noise we supplement the sound localization with a source detection stage. Also, we include a simple onset detector to retain fast response times. Since we only use two microphones, we are confronted with ambiguities on whether a source is in front or behind the device. We resolve these ambiguities with the help of face detection and additional moves. We tailor the system to our target scenarios in experiments with a four minute scripted conversation. In these experiments we evaluate the influence of different system settings on the responsiveness and accuracy of the device.