Развој машинског учења интелигентног мобилног робота базиран на систему вештачких неуронских мрежа
Machine learning of inteligent mobile robot based on arti ficial neural networks
Докторанд
Vuković, Najdan L.Ментор
Miljković, ZoranЧланови комисије
Milutinović, DraganBabić, Bojan R.
Aleksendrić, Dragan
Potkonjak, Veljko
Метаподаци
Приказ свих података о дисертацијиСажетак
Унутрашњи транспорт сировина, материјала и готових делова подразумева брзо, ефикасно и економично деловање постављеног транспортног задатка...
Material Handling Systems in manufacturing environment imply efficient
and economical transport solutions. Automated Guided Vehicles (AGVs) are a
common choice made by many companies for Material Handling in
manufacturing systems. Nowadays, AGV based internal transport of raw
materials, goods and parts is becoming improved with advances in technology.
Demands for fast, efficient and reliable transport imply the usage of the flexible
AGVs with onboard sensing and special kinds of algorithms needed for daily
operation. These transport solutions can be modified and enhanced by applying
advanced methods and technologies. New generation of internal transport
systems should operate autonomously, without direct human control. Level of
development of mobile robots insures reliability and efficiency needed for
dayily operations within manufacturing environemnt. In this thesis, the
implementation of mobile robots for internal transport within Material
Handling System is analyzed and new solution...s are proposed.
Focus of research efforts is devoted to the ability to estimate position and
orientation of mobile robot within manufacturing environment using newly
developed algorithms and sensory information. Simultaneous localization (of
the mobile robot) and mapping (of the working environment) is one of the most
important problems in mobile robotics community. The soultion to this
problem insures autonomous navigation and henceforth autonomous operation
for transport purposes within manufacturing/industrial facility without direct
human control. In this thesis, new algorithm for state estimation is proposed
and analyzed; the algorithm is based on integration of Extended Kalman Filter
and feedforward neural networks (Neural Extended Kalman Filter) and camera
is used as exteroceptive sensor. Furhermore, to achieve intelligent behavior, the
X
new robotic hybrid control architecture is developed and analyzed. Finally, the
new hybrid control algorithm for guidance of mobile robot is proposed. Two
building blocks form the hybrid algorithm: visual servoing and position based
control. Neural Extended Kalman Filter is used for state estimation of the
mobile robots, and at each time instant the robot knows its position and
orientation...
Факултет:
Универзитет у Београду, Машински факултетДатум одбране:
28-09-2012Пројекти:
- Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима (RS-MESTD-Technological Development (TD or TR)-35004)