Empirical control strategy for learning industrial robot

Današnji sistemi industrijskog robota intenzivno uključuju spoljašnje senzore kao što su kamere koje se koriste za identifikaciju objekata u radnom okruženju industrijskog robota. Uključivanjem spoljašnjih senzora-kamera problem upravljanja industrijskim robotom koji uči postaje značajno izražen. Korišćenjem empirijske upravljačke strategije, bazirane na sistemu veštačkih neuronskih mreža, industrijski robot koji uči može da ostvari adaptivno ponašanje u pogledu fleksibilnog prilagođavanja promenama u radnom okruženju. Pored prirodnih sistema koji mogu da uče na bazi iskustva, za veštačke sisteme se u dužem periodu govorilo da to nisu u stanju da ostvare. Ovaj rad ima za cilj da pokaže da je moguće ostvariti empirijsku upravljačku strategiju za industrijski robot koji uči, korišćenjem kamere i sistema veštačkih neuronskih mreža. Rezultati dobijeni korišćenjem sistema neuronskih mreža pokazali su da hvatač robota može da dođe u zahtevani položaj u odnosu na objekat hvatanja, čak i u slučaju kada je taj položaj različit od naučenih primera.Today's industrial robot systems intensively include external sensors like cameras used for identification of objects in the working environment of industrial robot. Including cameras in the system of an industrial robot, the control problem of such learning industrial robot is set. Using empirical control strategy based on application of artificial neural networks system the learning industrial robot can realize adaptive behavior in the sense of flexible adjustment to changes in the working environment. Unlike natural systems which could learn on the basis of experience, artificial systems are thought to be unable to do so for a long time. However, the concept of empirical control realizes the ability of machine learning on the basis of experience. This paper aims to show that it is possible to realize the empirical control strategy for learning industrial robot using camera and system of artificial neural networks. Results obtained by the system of neural nets have shown that the robot can move the end-effector to the desired location of the object, even in the case where the location differs slightly from the learned patterns

Machine-part family formation by using ART-1 Simulator and FLEXY

Tehnološki sistemi bazirani na konceptu grupne tehnologije imaju prednosti pre svega u domenu fleksibilnosti. U radu je, uvođenjem nove tehnike klasterovanja, analiziran odnos familije mašine-delovi unutar tehnološkog sistema i relevantnih tehnoloških procesa, s obzirom na tehnološku sličnost delova koji čine familiju. Takav tehnološki sistem se organizuje u grupe mašina, formirajući ćelije, uz obezbeđenu maksimalnu proizvodnost delova. Rad prezentira novu primenu ART-1 veštačke neuronske mreže u analizi tehnološke sličnosti i nudi modifikovan bazični pristup u cilju povećanja efikasnosti procedure klasifikovanja. Razvijeni softveri ART-1 Simulator i FLEXY su korišćeni u postupku formiranja familija, shodno reafirmisanom konceptu projektovanja grupne tehnologije.Group technology based manufacturing systems offer the advantages of flow production as well as the production flexibility of batch manufacturing. In this paper, by employing new clustering techniques, the part-machine spectrum of the manufacturing system and the relevant manufacturing process are analyzed according to design, similarity of machining and product flow. This leads to an organization of the production system into self-contained and self-regulating groups of machines called machine cells. Each machine cell undertakes a maximal production of a family of parts having similar manufacturing characteristics. This paper carried out the ART-1 neural network approach in the analysis of the manufacturing similarity, and modified the basic approach to increase the efficiency of the classification procedure. Developed program packages ART-1 Simulator and FLEXY are used to create part families and machine cells within the group technology design

Empirical control strategy for learning industrial robot

Današnji sistemi industrijskog robota intenzivno uključuju spoljašnje senzore kao što su kamere koje se koriste za identifikaciju objekata u radnom okruženju industrijskog robota. Uključivanjem spoljašnjih senzora-kamera problem upravljanja industrijskim robotom koji uči postaje značajno izražen. Korišćenjem empirijske upravljačke strategije, bazirane na sistemu veštačkih neuronskih mreža, industrijski robot koji uči može da ostvari adaptivno ponašanje u pogledu fleksibilnog prilagođavanja promenama u radnom okruženju. Pored prirodnih sistema koji mogu da uče na bazi iskustva, za veštačke sisteme se u dužem periodu govorilo da to nisu u stanju da ostvare. Ovaj rad ima za cilj da pokaže da je moguće ostvariti empirijsku upravljačku strategiju za industrijski robot koji uči, korišćenjem kamere i sistema veštačkih neuronskih mreža. Rezultati dobijeni korišćenjem sistema neuronskih mreža pokazali su da hvatač robota može da dođe u zahtevani položaj u odnosu na objekat hvatanja, čak i u slučaju kada je taj položaj različit od naučenih primera.Today's industrial robot systems intensively include external sensors like cameras used for identification of objects in the working environment of industrial robot. Including cameras in the system of an industrial robot, the control problem of such learning industrial robot is set. Using empirical control strategy based on application of artificial neural networks system the learning industrial robot can realize adaptive behavior in the sense of flexible adjustment to changes in the working environment. Unlike natural systems which could learn on the basis of experience, artificial systems are thought to be unable to do so for a long time. However, the concept of empirical control realizes the ability of machine learning on the basis of experience. This paper aims to show that it is possible to realize the empirical control strategy for learning industrial robot using camera and system of artificial neural networks. Results obtained by the system of neural nets have shown that the robot can move the end-effector to the desired location of the object, even in the case where the location differs slightly from the learned patterns

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

Machine-part family formation by using ART-1 Simulator and FLEXY

Tehnološki sistemi bazirani na konceptu grupne tehnologije imaju prednosti pre svega u domenu fleksibilnosti. U radu je, uvođenjem nove tehnike klasterovanja, analiziran odnos familije mašine-delovi unutar tehnološkog sistema i relevantnih tehnoloških procesa, s obzirom na tehnološku sličnost delova koji čine familiju. Takav tehnološki sistem se organizuje u grupe mašina, formirajući ćelije, uz obezbeđenu maksimalnu proizvodnost delova. Rad prezentira novu primenu ART-1 veštačke neuronske mreže u analizi tehnološke sličnosti i nudi modifikovan bazični pristup u cilju povećanja efikasnosti procedure klasifikovanja. Razvijeni softveri ART-1 Simulator i FLEXY su korišćeni u postupku formiranja familija, shodno reafirmisanom konceptu projektovanja grupne tehnologije.Group technology based manufacturing systems offer the advantages of flow production as well as the production flexibility of batch manufacturing. In this paper, by employing new clustering techniques, the part-machine spectrum of the manufacturing system and the relevant manufacturing process are analyzed according to design, similarity of machining and product flow. This leads to an organization of the production system into self-contained and self-regulating groups of machines called machine cells. Each machine cell undertakes a maximal production of a family of parts having similar manufacturing characteristics. This paper carried out the ART-1 neural network approach in the analysis of the manufacturing similarity, and modified the basic approach to increase the efficiency of the classification procedure. Developed program packages ART-1 Simulator and FLEXY are used to create part families and machine cells within the group technology design

Susceptibility of Campylobacter jejuni and Campylobacter coli isolated from animals and humans to tetracycline

Fifty five thermophilic Campylobacter spp. strains were isolated from cecum of broilers, cecum and colon of pigs and from human feces. The strains were identified as Campylobacter jejuni and Campylobacter coli. The more prevalent species in broilers and humans was C. jejuni and in pigs C. coli. In the framework of this study, sensitivity to tetracycline in isolated strains of C. jejuni and C. coli was tested by E-test. In 16 tested strains isolated from broilers, 56.25% were resistant to tetracycline. Resistance occured more frequent in C. coli strains (66.67%). In 15 strains of termophilic Campylobacter spp. isolated from pigs the percentage of resistant strains was 80%. Resistance was detected more often in C. coli (90.00%) isolates. The percentage of resistant C. jejuni strains from pigs was 60.00%. Resistance to tetracycline occurred in 29.17% of 24 thermophilic Campylobacter spp. strains isolated from humans. Generally, strains of thermophilic campylobacters, especially C. coli isolated in pigs are more frequent resistant to tetracycline than strains isolated in poultry and human. Therefore, attention should be directed to the tetracycline application monitoring in swine farming in order to prevent resistance appearance in animal strains and its subsequent spread to human strains

СИСТЕМИ ВЕШТАЧКИХ НЕУРОНСКИХ МРЕЖА У ПРОИЗВОДНИМ ТЕХНОЛОГИЈАМА

U monografiji „Sistemi veštačkih neuronskih mreža u proizvodnim tehnologijama”, dati su rezultati mašinskog učenja edukacionog industrijskog robota „MITSUBISHI Movemaster-EX” i antropomorfnog mobilnog robota nazvanog „Don Kihot” koji su potvrdili osnovanost hipoteze da mehanizmi mašinskog učenja, zasnovani na veštačkim neuronskim mrežama i konceptu veštačkog života, mogu da obezbede mehatronskom sistemu-robotu odgovarajuću autonomnost pri izvršavanju tehnološkog zadatka manipulacije prepoznatih objekata uopšte i u okviru montaže. U ovoj osmoj knjizi serije „Inteligentni tehnološki sistemi”, pokazano je kako se koriste heterogene veštačke neuronske mreže pri realizaciji lokomocionog neuronskog upravljačkog sistema insekt robota. Veštačke neuronske mreže mogu da se koriste i za grubo projektovanje tehnoloških procesa, tako da je u ovoj monografiji predstavljena primena „ART-1” mreže u projektovanju grupne tehnologije za osnosimetrične cilindrične delove. Prikazan je i sistem prepoznavanja robota baziran na „Sony” CCD kameri, kao i procesiranje digitalne slike objekata manipulacije korišćenjem robota „MITSUBISHI Movemaster-EX” i robota nazvanog „Don Kihot”, uz prethodno izveden postupak kalibracije kamere. Posebno je prikazan originalno razvijen Algoritam empirijskog upravljanja koji koristi mašinsko učenje i sistem prepoznavanja baziran na CCD kameri, odnosno procesiranju digitalne slike objekata snimljenih u sceni robota

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

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on epsilon insensitive support vector regression (epsilon-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of epsilon-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

Empirical control system development for intelligent mobile robot based on the elements of the reinforcement machine learning and axiomatic design theory

Ovaj rad predstavlja istraživanje autora u domenu koncepcijskog projektovanja upravljačkog sistema koji može da uči na osnovu sopstvenog iskustva. Sposobnost adaptivnog ponašanja pri izvršavanju postavljenog zadatka u realnim, nepredvidivim uslovima, jedan je od ključnih zadataka svakog inteligentnog robotskog sistema. U funkciji rešavanja ovog problema, predlaže se pristup baziran na učenju, i to kombinovanjem empirijske upravljačke strategije, mašinskog učenja ojačavanjem i aksiomatske teorije projektovanja. Predloženi koncept koristi najbolje osobine pomenutih teorijskih pristupa u cilju ostvarivanja optimalne odluke mobilnog robota za trenutno stanje sistema. Empirijska upravljačka teorija se, u ovom radu, a priori koristi u utvrđivanju idejnog rešenja za rešavanje problema navigacije mobilnog robota. Učenje ojačavanjem realizuje mehanizme koji memorišu i ažuriraju odgovore okruženja, a u kombinaciji sa empirijskom upravljačkom teorijom određuje najbolju moguću odluku u skladu sa trenutnim okolnostima. Aksiomatska teorija projektovanja se koristi pri definisanju upravljačkog problema, kao i pri uspostavljanju koncepcijskog rešenja za dati zadatak, sa aspekta primene pomenutih pristupa. Deo predloženog algoritma empirijskog upravljanja realizovan je pomoću LEGO Mindstorms NXT mobilnog robota, tretirajući problem navigacije u nepoznatom okruženju. Ostvareni eksperimentalni rezultati nagoveštavaju dobru perspektivu za realizaciju efikasnog upravljanja baziranog na iskustvu, čiji dalji razvoj može da dovede do ostvarenja autonomnog ponašanja mobilnog robota pri izbegavanju prepreka u tehnološkom okruženju, što je i očekivani naučni cilj.This paper presents the authors' efforts to conceptual design of control system that can learn from its own experience. The ability of adaptive behaviour regarding the given task in real, unpredictable conditions is one of the main demands for every intelligent robotic system. To solve this problem, the authors suggest a learning approach that combines empirical control strategy, reinforcement learning and axiomatic design theory. The proposed concept uses best features of mentioned theoretical approaches to produce optimal action in the current state of the mobile robot. In this paper empirical control theory imparts the basis of conceptual solution for the navigation problem of mobile robot. Reinforcement learning enables the mechanisms that memorize and update environment responses, and combining with the empirical control theory determines best possible action according to the present circumstances. Axiomatic design theory accurately defines the problem and possible solution for the given task in terms of the elements defined by two previously mentioned approaches. Part of the proposed algorithm was implemented on the LEGO Mindstorms NXT mobile robot for the navigation task in an unknown manufacturing environment. Experimental results have shown good perspective for development of efficient and adaptable control system, which could lead to autonomous mobile robot behaviour