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

Integration of process planning, scheduling, and mobile robot navigation based on triz and multi-agent methodology

U radu je predstavljena metodologija za razvoj softverske aplikacije za integraciju projektovanja tehnološkog procesa, terminiranja proizvodnje i navigacije mobilnog robota u tehnološkom okruženju. Predložena metodologija je bazirana na primeni teorije inventivnog rešavanja problema i multiagentske metodologije. Matrica kontradikcije i inventivni principi su se pokazali kao efektivan alat za otklanjanje kontradiktornosti u koncepcijskoj fazi razvoja softvera. Predložena multiagentska arhitektura sadrži šest agenata: agent za delove, agent za mašine, agent za optimizaciju, agent za planiranje putanje, agent za mašinsko učenje i agent mobilni robot. Svi agenti zajedno učestvuju u optimizaciji tehnološkog procesa, optimizaciji planova terminiranja, generisanju optimalnih putanja koje mobilni robot prati i klasifikaciji objekata u tehnološkom okruženju. Eksperimentalni rezultati pokazuju da se razvijeni softver može koristiti za predloženu integraciju, a sve u cilju poboljšanja performansi inteligentnih tehnoloških sistema.This paper presents methodology for development of software application for integration of process planning, scheduling, and the mobile robot navigation in manufacturing environment. Proposed methodology is based on the Russian Theory of Inventive Problem Solving (TRIZ) and multiagent system (MAS). Contradiction matrix and inventive principles are proved as effective TRIZ tool to solve contradictions during conceptual phase of software development. The proposed MAS architecture consists of six intelligent agents: job agent, machine agent, optimization agent, path planning agent, machine learning agent and mobile robot agent. All agents work together to perform process plans optimization, schedule plans optimization, optimal path that mobile robot follows and classification of objects in a manufacturing environment. Experimental results show that developed software can be used for proposed integration in order to improve performance of intelligent manufacturing systems