Umjetna inteligencija i robotika kao pokretačka snaga modernog društva

In synergy with other technologies, the AI significantly accelerates the scientific and technological development of human society. New possibilities of the application of technological achievements are constantly opening up – in industry, healthcare and everyday life. AI-based robotics is the main driver of the present industrial revolution. Robots have already played an important role in production and changed the production economy over the past decade. New generations of smart robots, or smart technical systems in general, are turning to new applications, especially in service industries, medicine and home use. In the future, autonomous and mobile robots will be able to assist the elderly and immobile, help with household chores, act as caregivers and perform repetitive, tedious or dangerous jobs in nursing homes, hospitals, military environments, disaster sites and schools. The potential benefits are great, but they pose significant ethical challenges too. Our autonomy may be compromised and social interaction obstructed. Expanded use of robots can lead to reduced contact among people and possible restrictions on personal freedoms. Machines of these kinds shape the new world radically, leading to significant economic and cultural changes, creating both winners and losers on a global scale.U sinergiji s drugim tehnologijama AI značajno ubrzava znanstveni i tehnološki razvoj ljudskog društva. Neprestano se otvaraju nove mogućnosti primjene tehnoloških dostignuća, kako u industriji, zdravstvu tako i u svakodnevnom životu. Robotika temeljena na umjetnoj inteligenciji glavni je pokretač sadašnje industrijske revolucije. Roboti su već odigrali važnu ulogu u proizvodnji i promijenili proizvodnu ekonomiju tijekom posljednjih desetak godina. Nove generacije pametnih robota, ili općenito pametnih tehničkih sustava, okreću se novim primjenama, posebno u uslužnim djelatnostima, medicini i kućnoj uporabi. Autonomni i mobilni roboti u budućnosti će moći pomagati starijim i nepokretnim osobama, pomagati u kućanskim poslovima, djelovati kao njegovatelji i obavljati ponavljajuće, dosadne ili opasne poslove u staračkim domovima, bolnicama, vojnim okruženjima, mjestima katastrofe i školama. Potencijalne prednosti su velike, ali također predstavljaju značajne etičke izazove. Naša autonomija može biti ugrožena, a društvena interakcija opstruirana. Prošireno korištenje robota može dovesti do smanjenog kontakta među ljudima i mogućih ograničenja osobnih sloboda. Strojevi ove vrste oblikuju radikalno novi svijet, što dovodi do značajnih ekonomskih i kulturoloških promjena, stvarajući jednako pobjednike kao i gubitnike na globalnoj svjetskoj razini

Planiranje robotskog djelovanja zasnovano na tumačenju prostornih struktura

Robot je programabilan mehanizam čije se djelovanje temelji na upravljačkim algoritmima. Prilikom rada u nestrukturiranoj okolini upravljački algoritmi postaju eksplicitne funkcije položaja i vremena u povratnoj vezi sa stanjem okoline. Obradu podataka iz okoline te zaključivanje o odgovarajućem djelovanju robota moguće je temeljiti na principima strojnoga učenja. Predloženo istraživanje bavi se razvojem modela učenja i planiranja djelovanja robota. Proces učenja temelji se na novoj umjetnoj neuronskoj mreži klasifikacijom prostornih struktura. Pojam prostorne strukture podrazumijeva interpretaciju rasporeda poznatih objekata u ravnini koje robot percipira vizijskim sustavom. Umjetna neuronska mreža za klasifikaciju i prepoznavanje prostornih struktura zasniva se na teoriji adaptivne rezonancije. Planiranje djelovanja robota temeljno je na usporednoj evoluciji rješenja razvojem novoga genetskoga algoritma. Genetski algoritam kao osnovni cilj ima prostornu pretvorbu neuređenoga stanja objekata u uređeno. Izvorni znanstveni doprinos rada očituje se u sljedećem: 1) Samoorganizirajuća umjetna neuronska mreža za klasifikaciju i prepoznavanje prostornih struktura zasnovana na teoriji adaptivne rezonancije, koju odlikuje nova dvorazinska klasifikacija po obliku i rasporedu objekata te mehanizam asocijativnoga povezivanja neuređenoga skupa objekata s uređenim i 2) Novi genetski algoritam za planiranje robotskoga djelovanja u nestrukturiranoj radnoj okolini karakteriziran usporednom evolucijskom strategijom za pronalaženje rješenja, s ciljem prostorne pretvorbe neuređenoga stanja objekata u uređeno

Object Tracking with a Multiagent Robot System and a Stereo Vision Camera

AbstractWhen working with a robot in terms of object manipulation the essential information is relative position between robot's tool center point (TCP) and the object of interest. This paper proposes a method of frame relative displacement and describes a working multiagent robot application that can be used for tracking, tooling or handling operations with the use of stereo vision in unstructured laboratory environment. Robot system is composed of two Fanuc robot arms, one of which carries a stereo vision camera system and the other which is guided in relation to object of interest. The latter robot has a marker that is used for navigation between the robot and the object of interest. Image processing, marker detection, 3-D coordinates extraction, coordinate system transformations, offset coordinates calculation and communication are handled using c++ multithread program and TCP/IP protocol

Tuning of Parameters for Robotic Contouring Based on the Evaluation of Force Deviation

The application of industrial robots with advanced sensor systems in unstructured environments is continuously becoming wider. A widely used type of advanced sensor systems is the force-torque sensor. Force-torque sensors are typically used for applications such as robot grinding, sanding, polishing, and deburring, where a constant force is exerted upon a workpiece. In this research, control parameters for exerting a constant force along a predefined path are evaluated in laboratory conditions. The experimental setup with the contouring force feedback is composed of a Fanuc LRMate six-degree-of-freedom industrial robot with an integrated force-torque sensor. Control parameters of the Contouring function within the Fanuc robot controller are tuned in four contouring experiments. The experiments conducted in this research are: i) flat beam, ii) flat beam with a rigid support, iii) wave shaped compliant plate, and iv) compliant flat plate. During the experiments, contouring parameters were altered in order to collect the feedback on the values of the force to be used for the evaluation of the force deviation. A fitness function for the evaluation of the force deviation and the tuning of the control parameters is presented. The fitness function enables a selection of initial control parameters which minimize the force deviation during the robot contouring process

A multiagent framework for industrial robotic applications

AbstractThe paper presents a novel approach toward modeling and governing complex system behavior in flexible and adaptive robotic assembly systems. A fully distributed multiagent approach is implemented for autonomous control. The system is defined at multiple levels of granularity where agents provide services in respect to the current global goal. A decentralized multiagent approach is adopted for reasons of flexibility and fault tolerance embedded in the design phase. To prove the concept a robotic application for intelligent assembly is presented and discussed. It consists of multiple industrial robots equipped with force/torque sensors, 2D and 3D vision systems, automatic tool changers and other sensors and actuators. Through fusion of sensory input and mutual communication agents construct and negotiate an assembly plan and reconfigure respectively

Task planning based on the interpretation of spatial structures

U ovom istraživanju razvijen je novi algoritam planiranja za transformaciju početnog neuređenog stanja objekata u uređeno konačno stanje. Zadatak algoritma planiranja je pronaći mogući niz djelovanja kojima se početno stanje okoline, kroz konačan broj diskretnih transformacija, može dovesti u zadano konačno stanje. Stanje okoline tumači se kroz položaj i orijentaciju objekata. Zadatak planiranja rješava se u dva koraka. Razvijena je konstruktivna heuristika pomoću koje se dobiva početni skup rješenja. Konstruktivna heuristika koristi mutacije za generiranje početne populacije. Genetski algoritam je razvijen za optimizaciju početnog skupa rješenja. Genetski algoritam karakteriziran je usporednom evolucijskom strategijom za pronalaženje rješenja, s ciljem prostorne pretvorbe neuređenog stanja objekata u uređeno, ograničen na dvodimenzionalnu interpretaciju radnog prostora. Verifikacija algoritma planiranja napravljena je u virtualnom okruženju.In this research, a new task planning algorithm is developed for building a desired object configuration from a given initial unordered object state. The task of the planning algorithm is to find a feasible set of actions, i.e. a finite number of discrete transformations, which can rearrange the objects into a desired ordered final state. The environment is interpreted through the position and orientation of the objects. The solution to the planning problem is proposed as a two-step method. First, a constructive heuristic generates an initial set of good solutions. The constructive heuristic uses only mutations for making an initial population of state transitions. A genetic algorithm is developed for optimizing the initial set of solutions. The genetic algorithm is characterized by a parallel evolutionary strategy, with the aim of spatial transformation of unordered object states into ordered object states. The algorithm can be used for solving the task planning problems represented in the two-dimensional space. Verification of the planning algorithm is done in a virtual environment