[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

Vision System for Group of Mobile Robots 155
4 Conclusions
The version of vision system presented in this work proved to be very successful
and fully operational. Proposed algorithm provides stable vision information i.e. it
never losses any objects, they are always tracked properly. Used histogram-based
color classifier proved to be very efficient and difficult to mislead, mainly because
of the algorithm’s ability to adapt stored reference histograms to changing lighting
according to the current classification. Color classifier operates properly in the
limited colorspace of the NTSC signal coming from cheap industrial camera.
It is very easy to find colors that can be used as markers on top of the robots.
The algorithm is sufficiently robust in assumed range of changes in the lighting
conditions, both spatial (caused by the nonuniform lighting around the playing
field, caused by the fact that there are four lamps above the corners of playing
field) and temporal (caused by accidents like turning off one of the lamps or opening
the window, which results in increased amount of sunlight coming to the playing
field).
Measurement data is precise enough to be used in sophisticated control loops,
and the delay introduced to the system is almost constant and in assumed range,
guarantying stability of the control algorithms. Relative measurement errors of
position coordinates x and y and angle of orientation do not exceed 1.5% and
delay is at most 30 ms, being the limitation of the frame-grabber card, which is
able to deliver only 30 images each second.
Performance of the application is also very satisfactory - the whole frame is
processed in 15 ms. It gives the possibility of obtaining high framerates (at least
60 frames per second) after upgrading the framegrabber card to the model that is
able to deliver more than 30 images per second.
The vision data is made available to many other applications via TCP/IP socket.
It makes possible building distributed control system for mobile robots, consisting
of many computing nodes. Application design is modular, individual components
are loosely coupled to each other and are implemented with use of design patterns,
which further simplify the design and promote good solutions, so further development
should be quite easy. This is particularly important, for the existing code
could be used in future industrial-quality vision application.
Acknowledgement. This work has been supported by Ministry of Science and Higher
Education In the years 2010 - 2012 as development project OR000132 12.
References
[1] Sridharan, M., Stone, P.: Autonomous Color Learning on a Mobile Robot. In: The
Twentieth National Conference on Artificial Intelligence (AAAI 2005), Pittsburgh,
USA (July 2005)
[2] Jeong, S.: Histogram-Based Color Image Retrieval Psych221/EE362 Project Report.
Stanford University (2001)