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 daily operations within manufacturing environment. In this thesis, the implementation of mobile robots for internal transport within Material Handling System is analyzed and new solutions 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 solution 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. The proposed solutions are developed in MATLAB® environment by developing a specific software code and tested using Khepera II mobile robot, WEB camera and LEGO Mindstorms NXT mobile robot. Simulation and experimental results show usability of the proposed solutions for material handling within an Intelligent Manufacturing System.
Unutrašnji transport sirovina, materijala i gotovih delova podrazumeva brzo, efikasno i ekonomično obavljanje postavljenog transportnog zadatka. Unutrašnji transport u okviru tehnološkog okruženja moguće je unaprediti uvođenjem naprednih metoda i tehnologija. Razvoj ovih sistema unutrašnjeg transporta treba da rezultira inteligentnim sistemima unutrašnjeg transporta koji su u stanju da sprovedu transportni zadatak bez direktnog nadzora od strane operatera. Primena mobilnih robota u okviru inteligentnog tehnološkog sistema za potrebe unutrašnjeg transporta omogućila bi efikasnije i ekonomičnije obavljanje postavljenog transportnog zadatka. Stepen razvoja i primene mobilnih robota dostigao je nivo neophodan za ispunjavanje zahteva koji su određeni tehnološkim i proizvodnim procesima. U istraživanjima sprovedenim u okviru ove doktorske disertacije razvijene su nove metode neophodne za primenu mobilnih robota u inteligentnom tehnološkom sistemu. Fokus istraživačkih napora predstavlja sposobnost mobilnog robota da uz primenu odgovarajuće matematičkosoftverske podrške i akvizicijom informacije od kamere, samostalno odredi svoj položaj i položaj karakterističnih objekata u okruženju. Problem simultanog ocenjivanja položaja mobilnog robota i karakterističnih objekata u okruženju rešen je primenom linearizovanog Kalmanovog filtra i veštačkih neuronskih mreža. Analizirana je i mogućnost ostvarivanja inteligentnog ponašanja mobilnog robota u formi razvoja hibridne upravljačke arhitekture, koja obezbeđuje robustnost zahvaljujući primeni koncepta mašinskog učenja na bazi veštačkih neuronskih mreža. Poseban segment itraživanja posvećen je razvoju hibridnog upravljačkog algoritma namenjenog za eksploataciju mobilnog robota u okviru inteligentnog tehnološkog sistema bez dodatne transportne infrastrukture. Predložene metode su verifikovane kroz razvoj simulacije u MATLAB® programskom okruženju i putem eksperimentalnog metoda. Eksperimentalni postupak je sproveden u laboratorijskom modelu tehnološkog okruženja korišćenjem LEGO Mindstorms NXT mobilnog robota i Khepera II bilnog robota uz primenu sistema prepoznavanja na bazi kamere. Simulacioni i eksperimentalni rezultati ukazuju da razvijene metode podržavaju predloženi koncept primene mobilnih robota za potrebe unutrašnjeg transporta u okviru iteligentnog tehnološkog sistema.
