New hybrid control architecture for intelligent mobile robot navigation in a manufacturing environment

U radu je prikazana nova hibridna upravljačka arhitektura namenjena za eksploataciju i navigaciju inteligentnih mobilnih robota u tehnološkom okruženju. Arhitektura je bazirana na empirijskom upravljanju i implementaciji koncepta mašinskog učenja u vidu razvoja sistema veštačkih neuronskih mreža za potrebe generisanja inteligentnog ponašanja mobilnog robota. Za razliku od konvencionalne metodologije razvoja inteligentnih mobilnih robota, predložena arhitektura je razvijena na temeljima eksperimentalnog procesa i implementacije sistema veštačkih neuronskih mreža za potrebe generisanja inteligentnog ponašanja. Predložena metodologija razvoja i implementacije inteligentnih mobilnih robota treba da omogući nesmetanu i pouzdanu eksploataciju ali i robustnost u pogledu generisane upravljačke komande, kao odgovora robota na trenutno stanje tehnološkog okruženja.This paper presents a new hybrid control architecture for Intelligent Mobile Robot navigation based on implementation of Artificial Neural Networks for behavior generation. The architecture is founded on the use of Artificial Neural Networks for assemblage of fast reacting behaviors, obstacle detection and module for action selection based on environment classification. In contrast to standard formulation of robot behaviors, in proposed architecture there will be no explicit modeling of robot behaviors. Instead, the use of empirical data gathered in experimental process and Artificial Neural Networks should insure proper generation of particular behavior. In this way, the overall architectural response should be flexible and robust to failures, and consequently provide reliableness in exploitation. These issues are important especially if one takes under consideration that this particular architecture is being developed for mobile robot operating in manufacturing environment as a component of Intelligent Manufacturing System

Variational inference for robust sequential learning of multilayered perceptron neural network

U radu je prikazan i izveden novi sekvencijalni algoritam za obučavanje višeslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju značajan problem, posebno ukoliko sprovodimo sekvencijalno obučavanje ili obučavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistički generativni model u kome je matrica kovarijansi šuma merenja modelovana kao stohastički proces, a apriorna informacija usvojena kao inverzna Višartova raspodela. Izvođenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se rešio korak modifikacije primenjen je varijacioni metod, u kome rešenje problema tražimo u familiji raspodela odgovarajuće funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni korišćenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greške na test skupu podataka. Prosečna vrednost poboljšanja određena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm

Machine learning of radial basis function neural network based on Kalman filter: Implementation

U ovom radu su prikazani eksperimentalni rezultati primene tri nova sekvencijalna algoritma mašinskog učenja u cilju optimizacije parametara veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa na bazi Kalmanovog filtra. Uvedena su tri nova sekvencijalna algoritma mašinskog učenja: linearizovani Kalmanov filtar, linearizovani informacioni filtar, algoritam specifične aproksimacije momenata Gausove raspodele. Nakon prikaza odgovarajućih matematičkih modela datih u prvom delu ovog rada, u ovom delu razvijeni algoritmi su testirani u MATLAB® programskom okruženju razvojem odgovarajućeg softverskog koda i primenom test skupova podataka. Iako svi izabrani test skupovi podataka predstavljaju realne probleme, razvijeni algoritmi su testirani i na realnom inženjerskom problemu modeliranja izgleda segmenta obrađene površine. Sva tri algoritma su prilikom modeliranja ovih problema pokazala visok stepen tačnosti.In this paper we test three new sequential machine learning algorithms for radial basis function (RBF) neural network based on Kalman filter theory. Three new algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. Having introduced and derived mathematical model of each algorithm in the previous part of the paper, in this part we test and assess their performance using standard test sets from machine learning community. RBF neural network and three developed algorithms are implemented in MATLAB® programming environment. Experimental results obtained on real data sets as well as on real engineering problem show that developed algorithms result in more accurate models of the problem being investigated based on radial basis function neural network

Variational inference for robust sequential learning of multilayered perceptron neural network

U radu je prikazan i izveden novi sekvencijalni algoritam za obučavanje višeslojnog perceptrona u prisustvu autlajera. Autlajeri predstavljaju značajan problem, posebno ukoliko sprovodimo sekvencijalno obučavanje ili obučavanje u realnom vremenu. Linearizovani Kalmanov filtar robustan na autlajere (LKF-RA), je statistički generativni model u kome je matrica kovarijansi šuma merenja modelovana kao stohastički proces, a apriorna informacija usvojena kao inverzna Višartova raspodela. Izvođenje svih jednakosti je bazirano na prvim principima Bajesovske metodologije. Da bi se rešio korak modifikacije primenjen je varijacioni metod, u kome rešenje problema tražimo u familiji raspodela odgovarajuće funkcionalne forme. Eksperimentalni rezultati primene LKF-RA, dobijeni korišćenjem stvarnih vremenskih serija, pokazuju da je LKF-RA bolji od konvencionalnog linearizovanog Kalmanovog filtra u smislu generisanja niže greške na test skupu podataka. Prosečna vrednost poboljšanja određena u eksperimentalnom procesu je 7%.We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is given as inverse Wishart distribution. Derivation of expressions comes straight form first principles, within Bayesian framework. Analytical intractability of Bayes' update step is solved using Variational Inference (VI). Experimental results obtained using real world stochastic data show that MLP network trained with proposed algorithm achieves low error and average improvement rate of 7% when compared directly to conventional EKF learning algorithm

Machine learning of radial basis function neural network based on Kalman filter: Implementation

U ovom radu su prikazani eksperimentalni rezultati primene tri nova sekvencijalna algoritma mašinskog učenja u cilju optimizacije parametara veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa na bazi Kalmanovog filtra. Uvedena su tri nova sekvencijalna algoritma mašinskog učenja: linearizovani Kalmanov filtar, linearizovani informacioni filtar, algoritam specifične aproksimacije momenata Gausove raspodele. Nakon prikaza odgovarajućih matematičkih modela datih u prvom delu ovog rada, u ovom delu razvijeni algoritmi su testirani u MATLAB® programskom okruženju razvojem odgovarajućeg softverskog koda i primenom test skupova podataka. Iako svi izabrani test skupovi podataka predstavljaju realne probleme, razvijeni algoritmi su testirani i na realnom inženjerskom problemu modeliranja izgleda segmenta obrađene površine. Sva tri algoritma su prilikom modeliranja ovih problema pokazala visok stepen tačnosti.In this paper we test three new sequential machine learning algorithms for radial basis function (RBF) neural network based on Kalman filter theory. Three new algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. Having introduced and derived mathematical model of each algorithm in the previous part of the paper, in this part we test and assess their performance using standard test sets from machine learning community. RBF neural network and three developed algorithms are implemented in MATLAB® programming environment. Experimental results obtained on real data sets as well as on real engineering problem show that developed algorithms result in more accurate models of the problem being investigated based on radial basis function neural network

Machine learning of inteligent mobile robot based on arti ficial neural networks

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 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 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..

Machine learning of radial basis function neural network based on Kalman filter: Introduction

U ovom radu se analizira problem mašinskog učenja veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa na bazi Kalmanovog filtra. Prikazana su tri nova sekvencijalna algoritma mašinskog učenja: prvi algoritam direktno primenjuje linearizovani Kalmanov filtar kao algoritam mašinskog učenja, drugi algoritam primenjuje dual Kalmanovom filtru pod nazivom linearizovani informacioni filtar, dok treći algoritam na poseban način aproksimira prvi i drugi moment Gausove raspodele. U radu se naglašavaju osnovne prednosti koje pomenuti algoritmi imaju u poređenju sa konvencionalnim vidovima mašinskog učenja. Za sva tri algoritma razvijen je odgovarajući matematički model veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa. Analizirane su osnovne postavke izvedenih algoritama u cilju njihove primene na složene probleme u inženjerskoj praksi.This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice

Machine learning of radial basis function neural network based on Kalman filter: Introduction

U ovom radu se analizira problem mašinskog učenja veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa na bazi Kalmanovog filtra. Prikazana su tri nova sekvencijalna algoritma mašinskog učenja: prvi algoritam direktno primenjuje linearizovani Kalmanov filtar kao algoritam mašinskog učenja, drugi algoritam primenjuje dual Kalmanovom filtru pod nazivom linearizovani informacioni filtar, dok treći algoritam na poseban način aproksimira prvi i drugi moment Gausove raspodele. U radu se naglašavaju osnovne prednosti koje pomenuti algoritmi imaju u poređenju sa konvencionalnim vidovima mašinskog učenja. Za sva tri algoritma razvijen je odgovarajući matematički model veštačke neuronske mreže sa radijalnim aktivacionim funkcijama Gausovog tipa. Analizirane su osnovne postavke izvedenih algoritama u cilju njihove primene na složene probleme u inženjerskoj praksi.This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice

Simulation of the Insect Robot Walking Over Level Terrain

This paper presents a simulation procedure for generating a gait pattern and lateral motion sequence of a six legged robot. Gait pattern was studied and presented in the horizontal plane while lateral motion sequences were analyzed in the sagittal plane. Both analytical and graphical methods are used to study and to present gait and motion analysis over level terrain. Walking over level terrain demands particular gait selection, which is different from gaits for walking over irregular terrain. On one hand, restricting robot’s motion onto level terrain can be seen as restricting mobility of the hexapod model by neglecting the gaits for irregular terrain and their properties. On the other hand, focusing on the level terrain gaits provides a chance for development of the model. Finally, through Matlab® simulation, the usefulness of proposed simulation procedure is shown and results of the simulation are given

Koncepcijsko projektiranje inteligentnog unutarnjeg transporta materijala korištenjem umjetne inteligencije

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matlab© software package is used for developing genetic algorithms, manufacturing process simulation, implementing search algorithms and neural network training. The obtained paths are tested by means of the Khepera II mobile robot system within a static laboratory model of manufacturing environment. The experiment results clearly show that an intelligent mobile robot can follow paths generated by using genetic algorithms as well as learn and predict optimal material transport flows thanks to using neural networks. The achieved positioning error of the mobile robot indicates that the conceptual design approach based on the axiomatic design theory can be used for designing the material transport and handling tasks in intelligent manufacturing systems.Pouzdan i efikasan transport materijala je jedan od ključnih zahtjeva koji utječe na povećanje produktivnosti u industriji. Iz tog razloga, u radu su predložena dva pristupa za inteligentan transport materijala korištenjem mobilnog robota. Prvi pristup se zasniva na primjeni genetskih algoritama za optimizaciju tehnoloških procesa. Optimalna putanja se dobiva korištenjem optimalnih tehnoloških procesa i genetskih algoritama za vremensko planiranje, uz minimalno vrijeme kao kriterij. Drugi pristup je temeljen na primjeni teorije grafova za generiranje putanja i neuronskih mreža za učenje generirane putanje. Matlab© softverski paket je korišten za razvoj genetskih algoritama, simulaciju tehnoloških procesa, implementaciju algoritama pretraživanja i obučavanje neuronskih mreža. Dobivene putanje su testirane pomoću Khepera II mobilnog robota u statičkom laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju kako inteligentni mobilni robot prati putanje generirane korištenjem genetskih algoritama, kao i da uči i predviđa optimalne tokove materijala zahvaljujući neuronskim mrežama. Ostvarena pogreška pozicioniranja mobilnog robota ukazuje da se koncepcijski pristup baziran na aksiomatskoj teoriji projektiranja može koristiti u projektiranju transporta i manipulacije u inteligentnom tehnološkom sustavu