[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

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140 A. Babiarz, R. Bieda, and K. Jaskot hundreds of fascinating and challenging problems from the domain of mechanics, electronics, automatic control and artificial intelligence that have to be solved and their solutions implemented. One of these problems is the task of building proper vision system, whose role and importance is briefly discussed in the next section. The general structure of the complete system for controlling a group of mobile robots is presented on the Figure1. The system consists of playing field (1.5x1.3 m), camera (mounted about 2 m above the playing field), vision system ( acquiring images from the camera via frame-grabber card), controller1 which is actually an AI module, responsible for taking all decisions; and radio transmitter, which routes the control decisions made by the AI to the proper robots via radio link. The approach could be extended to multiple types of information and mobile robots [10]. Fig. 1. Placement of vision system in the RoboSoccer control loop There are two teams with three mobile robots in each. Every robot is a cube having dimension 7.5x7.5x7.5 cm. Teams play with orange golf ball. The robots have black walls and black top cover with colorful marker on it. The black is used because of the simplicity of algorithm removing unneeded parts of the scene during vision processing. From the control algorithm’s point of view, the vision system works as the feedback loop, providing measurements of values being inputs to the controller. Because the vision processing is done on the on the host computer and not on board of a robot, it is possible to develop quite sophisticated and very precise vision algorithm, as the computational power of contemporary PCs is thousands as big as of micro-controllers used on-board the robots. Vision system could be also used for planning collision free path of other types of robots e.g. UAVs [7], [11]. Such problem is often solved with the methods of A.I [8].
Vision System for Group of Mobile Robots 141 2 Implementation of Vision Algorithm The algorithm operates in two modes: Learning Mode and Running Mode. This is typical approach in the domain of computer vision, however there are some efforts to get rid of the learning phase and to build an algorithm capable of autonomous online learning [1]. Before the vision algorithm can actually be used it requires short learning phase. The learning is supervised, however there are no very complex tasks that the user have to perform. The algorithm requires user’s attention in order to set proper thresholds for shadow removal and background subtraction algorithms and for placing the robots and ball in specific place on the playing field to discover unique color labels denoting every object of interest. As the algorithm is very robust, the threshold values have quite big tolerance and usually default values are sufficient. The block diagram of the steps of learning phase of the algorithm is presented on Figure 2. Fig. 2. Schematic diagram of the steps of learning phase of the vision algorithm After finished learning the algorithm is immediately ready for operation. The block diagram of the running mode of the algorithm is presented on the Figure 3. The algorithm starts with obtaining RGB image in resolution 320x240. This size comes from the limitations imposed by the frame grabber hardware used during the experiments. Resolution 640x480 is available only in interlaced mode and 640x240 has non-square pixels (twice as tall as wide). The next operation done on the image is shadow removal according to the current background model and selected threshold. Details of this algorithm are described below in separate section. Improved input image without shadows is then used to extract foreground objects by simple thresholding the difference to the current background model. Obtained binary image is a subject to mathematical morphology operator called dilation, which significantly improves the continuity of foreground objects, which usually become worse when an object is placed over a white line drawn on the black playing field. The dilation operator uses4-neighbourhood and a mask of size 3x3.
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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344 Conclusions Control algorithms
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[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

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