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

Towards a conceptual design of intelligent material transport using artificial intelligence

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 Matla

Towards a conceptual design of intelligent material transport using artificial intelligence

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 Matla

The base conception of one three-axes machine prototype with a parallel kinematics

Konfigurisan je jedan prototip troosne (3D) mašine sa paralelnom kinematikom (MPK). Sprovedena je ideja da se iskoriste tradicionalni resursi sa numeričkim upravljanjem na netradicionalan način. Na raspolaganju je bio jedan horizontalni obradni centar (ILR HBG80), CAD/CAM sistem (Pro/Engineer) za projektovanje proizvoda i programiranje obrade delova i oprema za komunikaciju obradnog centra sa radnom stanicom (CP NiceLink). Tako je taj postojeći tradicionalni resurs iskorišćen za pogon i upravljanje MPK. Data je osnovna koncepcija troosne MPK sa uzajamno upravnim pogonskim osama. Opisano je rešavanje inverznog i direktnog kinematičkog problema. Pokazane su granice radnog prostora za jedan položaj završnog člana na platformi. Postavljena analiza ove koncepcije MPK omogućila je uprošćavanje rešenja inverznog kinematičkog problema (IKP) za potrebe programiranja, kalibracije koordinatnih sistema baze i platforme i modeliranja grešaka i kompenzacija ovakve mašine. Ceo postupak koncipiranja ovog prototipa vršen je po pravilima simultanog inženjerstva korišćenjem jednog specifičnog CAD/CAM/CAE sistemaOne three-axes (3D) machine prototype with a parallel kinematics (MPK) is established. The idea of using traditional resources with a machining center (ILR HBG80), a CAD/CAM system (Pro/Engineer) for designing of the products and programming of work pieces machining, as well as an outfit for the communication of the machining center with the work station (CP Nice Link) was on disposal. That was the way of using the traditional resource for driving and controlling of the MPK. The base conception for the three-axes MPK with mutual-normal driving axes is given. The solution for the inverse and direct kinematics problem is described. The working space borders are given for one position of an end effector at the platform. Stated analyze of the described conception of the MPK, enabled simplification of the solution for the inverse kinematics problem (IKP) for the needs of programming, calibration of coordinate systems of a base and a platform and for modeling of errors and compensations of the machine. The whole conception system for the prototype is made under the rules of concurent engineering by using a specific CAD/CAM/CAE system.COBISS.SR-ID 11270913

Artificial neural networks and axiomatic design theory in conceptual design of intelligent material transport

У раду је представљен метод концепцијског пројектовања роботизованог унутрашњег транспорта материјала, базиран на аксиоматској теорији пројектовања и вештачкој интелигенцији. Метод комбинује примену алгоритама за генерисање путања кретања интелигентног мобилног робота и вештачке неуронске мреже за предикцију стања технолошког процеса и машинско учење транспортних путева материјала сходно пројектованим производним процесима. Симулација технолошког процеса, обучавање вештачких неуронских мрежа, као и реализација управљачког кода извршена је у софтверском пакету Matlab. Експериментални резултати на систему мобилног робота Khepera II показују да мобилни робот планира, учи и остварује оптималну путању кретања.This paper presents a method for conceptual design of material transport using mobile robot, based on axiomatic design theory and artificial intelligence. The method combines the use of algorithms to generate motion path of intelligent mobile robot as well as artificial neural networks for prediction of the manufacturing process and machine learning of material transport routes that are designed according to proposed production processes. Simulation of manufacturing processes, artificial neural networks training and implementation of algorithms is executed in Matlab software package. Experimental results on a system of Khepera II mobile robot show that mobile robot can plan, learn and make the optimal path

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Towards Implementation of Intelligent Mobile Robots in a Manufacturing Environment

This paper presents experimental results of intelligent mobile robot implementation for material transport in a manufacturing environment. Conventional material transport solutions are based on Automated Guided Vehicles. However, constant technological development and changes on the market affect implemented production policies and imply usage of more intelligent transport solutions. The paper provides experimental results of a robotic control architecture founded on a hybrid deliberative/reactive approach, built to achieve intelligent transport having Intelligent Transportation System as the final outcome. Experiments were conducted in a laboratory model of an existing manufacturing environment. Although results show applicability of architecture and provide glimpse of a real world implementation, some questions are still unanswered and further research is needed to meet all requirements of this task

Industrial mobile robots in intelligent manufacturing systems

U ovom radu se razmatra integracija industrijskih mobilnih robota u postojeće proizvodne paradigme kao što su CIM (Computer Integrated Manufacturing) i IMS (Intelligent Manufacturing Systems). Prilikom ove analize poseban akcenat je dat problemu unutrašnjeg transporta materijala u okviru pogona. U tom smislu problem lokalizacije industrijskih mobilnih robota identifikovan je kao jedan od osnovnih problema. U radu je prikazan algoritam lokalizacije izveden na osnovama Kalmanovog filtera. Izvedeni algoritam implementiran je u Matlab okruženju i izvršena je simulacija. Rezultati simulacije, diskusija i zaključak ukazuju na značaj i važnu ulogu mobilnih robota u inteligentnim tehnološkim sistemima.This paper analyzes integration of industrial mobile robots into manufacturing paradigms well known as CIM (Computer Integrated Manufacturing) and IMS (Intelligent Manufacturing Systems). Material transport was in the focus of the analysis. Having this in mind, the localization of industrial mobile robots is identified as one of the essential problems. For these purposes localization algorithm based on Kalman filter is presented. The paper provides simulation procedure in Matlab environment and presents results of mobile robot localization. The results of simulation, discussion and conclusion point out the significance and important role of mobile robots in IMS

Towards Implementation of Intelligent Mobile Robots in a Manufacturing Environment

This paper presents experimental results of intelligent mobile robot implementation for material transport in a manufacturing environment. Conventional material transport solutions are based on Automated Guided Vehicles. However, constant technological development and changes on the market affect implemented production policies and imply usage of more intelligent transport solutions. The paper provides experimental results of a robotic control architecture founded on a hybrid deliberative/reactive approach, built to achieve intelligent transport having Intelligent Transportation System as the final outcome. Experiments were conducted in a laboratory model of an existing manufacturing environment. Although results show applicability of architecture and provide glimpse of a real world implementation, some questions are still unanswered and further research is needed to meet all requirements of this task