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

AUTONOMOUS ROBOT LEARNING MODEL BASED ON VISUAL INTERPRETATION OF SPATIAL STRUCTURES

The main concept of the presented research is an autonomous robot learning model for which a novel ARTgrid neural network architecture for the classification of spatial structures is used. The motivation scenario includes incremental unsupervised learning which is mainly based on discrete spatial structure changes recognized by the robot vision system. The learning policy problem is presented as a classification problem for which the adaptive resonance theory (ART) concept is implemented. The methodology and architecture of the autonomous robot learning model with preliminary results are presented. A computer simulation was performed with four input sets containing 22, 45, 73, and 111 random spatial structures. The ARTgrid shows a fairly high (>85%) match score when applied with already learned patterns after the first learning cycle, and a score of >95% after the second cycle. Regarding the category proliferation, the results are compared with a more predictive modified cluster centre seeking algorithm

Learning manipulative skills using an artificial intelligence approach.

The aim of this research was to design a non-linear controller based on an Artificial Neural Network and Reinforcement Learning algorithms implementation, which is able to perform an intelligent robotic assembly of mechanical components. Different information was applied and combined to develop a fully unsupervised, intelligent controller. In the author's design no class labelling or geometry feature pretraining takes place. Only force and torque signals together with the direction of insertion were supplied to the controller. A unique sandwich structure of the intelligent controller was proposed. It featured two major layers, a State Recognition module where the detection and localisation of the contact points were performed, and the Decision Making subsystem where the decision about the next action took place.All the algorithms were implemented and tested on simulated data before being applied to the real-life peg-in-hole insertion. The results are presented in the form of graphs and tables.Evaluation of the environmental uncertainty was accomplished. The signal from the force and torque sensor was acquired under controlled conditions. All the data was collected to establish the area and level of uncertainty (e.g. signal errors) the artificial controller would need to learn to cope with and compensate for.The empirical part of the thesis includes the investigation into the effects of different learning methods applied on the same geometry. The influence of action-selection methods on AI agent performance was analysed. The proposed controller was applied to a set of real life peg-and-hole experiments. Both circular and square peg geometries were used, and insertions into chamfered and non-chamfered holes were performed. Materials with different friction factors were used for mating parts.Fast and stable knowledge acquisition was clearly present in all the cases investigated. A significant reduction in contact force value during the initial stage of the learning process was recorded. The force was usually reduced to one tenth of the initial value. Some fluctuations were recorded but when the cylindrical peg was considered the value of contact forces never exceeded 0.5 N during the steady state

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