Papers by Fahrudin Mehmedovic
This paper presents implementation of intelligent search strategy (ISS) based on genetic algorith... more This paper presents implementation of intelligent search strategy (ISS) based on genetic algorithm in verification of assembly process in the presence of uncertainties. The research platform is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly and favourable influence of vibration and rotation movement on compensation of tolerance was also observed. The approach is based on search in states, whose dimensions include relevant state variables (amplitude and frequencies of robots gripe vibration). The path replanner has evolved to be suitable for the situation of unexpected events due to uncertainties.
Journal of Computing and Information Technology
As an inspiration for robot behavior, it is possible to make analogies between behavior of biolog... more As an inspiration for robot behavior, it is possible to make analogies between behavior of biological organisms and robot models. Inspired by the behavior of the beetles, we designed reactive, sensor-based behavior for mobile robot. The focus is on intelligent control algorithm, which approximates the behavior of the dor beetle with a Finite State Machine-based Design method. This finite state machine–based approach could be usefull as methodologie for the improved planning of the real-time complex tasks in robot-based manufactoring systems, using information from factory sensor networks, and taking into account the constraints from factory environments. In this paper, we will present the motor control results obtained through experiments, which confirm the effectiveness of the control based on behavior algorithm of living organisms.
Cybernetics and Information Technologies, 2015
Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the int... more Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs of vehicles. The V2VSPP was trained and evaluated by using traffic data provided by the Australian Centre for Field Robotics. It contains vehicle state information, vehicle-to-vehicle communications and road maps with high temporal resolution for large numbers of interacting vehicles over a long time period. The experimental results show that the proposed approach (V2VSPP) has the capability of providing accurate predictions of speed profiles in multi-vehicle trajectories setup.
Automatika ‒ Journal for Control, Measurement, Electronics, Computing and Communications, 2013
This paper investigates the genetic based re-planning search strategy, using neural learned vibra... more This paper investigates the genetic based re-planning search strategy, using neural learned vibration behavior for achieving tolerance compensation of uncertainties in robotic assembly. The vibration behavior was created from complex robot assembly of cogged tube over multistage planetary speed. Complex extensive experimental investigations were conducted for the purpose of finding the optimum vibration solution for each planetary stage reducer in order to complete the assembly process in defined real-time. However, tuning those parameters through experimental discovering for improved performance is a time consuming process. Neural network based learning was used to generate wider scope of parameters in order to improve the robot behavior during each state of the assembly process. As a novel modelling formalism of reactive hybrid automata, we propose the Wormhole Model with both learning and re-planning capacities (WOMOLERE). For our application, the states of hybrid automaton include amplitudes and frequencies of robot vibration module. The transition action is a function of minimal distance and uncertainty effects due to jamming during the assembly process. The results suggest that the methodology is adequate and could be recognized as an idea for designing of robot surgery assistance methods, especially in soft-robotics.
Applied Computational Intelligence and Soft Computing, 2012
This paper presents a visual/motor behavior learning approach, based on neural networks. We propo... more This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.
Journal of Robotics, 2011
This paper presents implementation of optimal search strategy (OSS) in verification of assembly p... more This paper presents implementation of optimal search strategy (OSS) in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration) and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.
2011 XXIII International Symposium on Information, Communication and Automation Technologies, 2011
This paper presents a visual/motor behavior learning approach, based on neural networks. The beha... more This paper presents a visual/motor behavior learning approach, based on neural networks. The behavior description is introduced in order to create behavior learning. Our behaviorbased system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for off-line learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to gripe a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest the methodology is adequate and could be recognized as an idea for designing mobile robot assistance for blind people guiding or for performing manipulation tasks, as a helping hand to disabled people.
Anomaly detection refers to the problem of finding patterns in data that do not conform to expect... more Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. It is very important to timely detect parameter anomalies in real-world running thermal power plant system, which is one of the most complex dynamical systems. Artificial neural networks are one of anomaly detection techniques. This paper presents the Elman recurrent neural network as method to solve the problem of parameter anomaly detection in selected sections of thermal power plant (steam superheaters and steam drum). Inputs for neural networks are some of the most important process variables of these sections. In addition to the implementation of this network for anomaly detection, the effect of key parameter change on anomaly detection results is also shown. Results confirm that recurrent neural network is good approach for anomaly detection problem, especially in real-time industrial applications.
Uploads
Papers by Fahrudin Mehmedovic