Abstract
The control system of mobile robot (functioning in buildings and structures), based on the methods of trajectories construction using a depth gauge sensor Kinect. The general approach of the work is based on the sequential execution of three phases: obstacle detection, mapping and construction barriers trajectories. Recognition guidelines robot is implemented using an SURF algorithm and FLANN library. Their combined use provides effective recognition obstacle in the near zone of the mobile robot to the required accuracy in real time. Mapping obstacles (concave shells) and the estimate of the distance to the border barriers is based on getting the so-called acceptable points. For this purpose, using the algorithm implemented Point-cloud detection of different types of obstacles in the close range of the robot. Synthesized a modified 3D-pointcloud algorithm which provides a solution to the problem of the robot move without colliding with obstacles. These algorithms can be applied to any stereo cameras and rangefinders that return colored frames and image depth, as described algorithms use data on the distance of objects from the sensor and are resistant to the visible spectrum of light (can operate in total darkness, without requiring a backlight). Built three-level robot control system in buildings and structures on the basis of a modified algorithm of 3D-pointcloud. The results of experiments confirming the effectiveness of the above approach.
Highlights
This article describes methods of solving problems of automatic navigation of robotized mobile devices inside of a building
Micro controller based on Arduino is responsible for receiving
processing of information taken from sensors
Summary
Pаспознавание оpиентиpов (объектов) в потоке виäеоäанных явëяется сëожной заäа÷ей, äëя pеøения Pис. 4. Эффективностü нахожäения объектов с испоëüзованиеì аëãоpитìа SURF ìожно увеëи÷итü за с÷ет совìестноãо пpиìенения Pис. 5. Пpимеp постpоения каpты пpепятствий бибëиотеки FLANN Эффективностü обнаpужения пpепятствий (поäвижных и непоäвижных объектов) в pежиìе pеаëüноãо вpеìени с пpиìенениеì 3D-обëака то÷ек äоказана в pаботе [17]. Обëака то÷ек хpанят кооpäинаты всех то÷ек в бëижней зоне ìобиëüноãо pобота, а также RGB-öвета, зна÷ения интенсивности и т. Ä. 3D-обëака то÷ек ìоãут бытü поëу÷ены из каpты ãëубины изобpажения, поëу÷енной от äат÷ика Kinect 5. Аëãоpитì обеспе÷ивает pеøение тpех поäзаäа÷: z поëу÷ение инфоpìаöии о пpепятствиях; z вы÷исëение pасстояний от äат÷ика Kinect äо пpепятствий и наиìенüøих pасстояний ìежäу пpепятствияìи; z поëу÷ение тpебуеìых то÷ек: поëу÷ение ãpаниö пpепятствия; z соpтиpовка pасстояний от äат÷ика Kinect äо кажäоãо пpепятствия в поpяäке от бëижайøеãо к наибоëее уäаëенноìу, а также фоpìиpование спи-. Пpи этоì пpиìенение аëãоpитìа понижения pазpеøения и DBSCAN (пëотностный аëãоpитì кëастеpизаöии пpостpанственных äанных с пpисутствиеì øуìа) [19] существенно увеëи÷ивает быстpоäействие аëãоpитìа pаспознания объектов и повыøает еãо то÷ностü
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