Á Ballagi

24586723400

Publications - 53

Adaptive Sign Language Recognition for Deaf Users: Integrating Markov Chains with Niching Genetic Algorithm

Saad M. Darwish Á Ballagi Oday Ali Hassen Muslem Al-Saidi

Publication Name: AI Switzerland

Publication Date: 2025-08-01

Volume: 6

Issue: 8

Page Range: Unknown

Description:

Sign language recognition (SLR) plays a crucial role in bridging the communication gap between deaf individuals and the hearing population. However, achieving subject-independent SLR remains a significant challenge due to variations in signing styles, hand shapes, and movement patterns among users. Traditional Markov Chain-based models struggle with generalizing across different signers, often leading to reduced recognition accuracy and increased uncertainty. These limitations arise from the inability of conventional models to effectively capture diverse gesture dynamics while maintaining robustness to inter-user variability. To address these challenges, this study proposes an adaptive SLR framework that integrates Markov Chains with a Niching Genetic Algorithm (NGA). The NGA optimizes the transition probabilities and structural parameters of the Markov Chain model, enabling it to learn diverse signing patterns while avoiding premature convergence to suboptimal solutions. In the proposed SLR framework, GA is employed to determine the optimal transition probabilities for the Markov Chain components operating across multiple signing contexts. To enhance the diversity of the initial population and improve the model’s adaptability to signer variations, a niche model is integrated using a Context-Based Clearing (CBC) technique. This approach mitigates premature convergence by promoting genetic diversity, ensuring that the population maintains a wide range of potential solutions. By minimizing gene association within chromosomes, the CBC technique enhances the model’s ability to learn diverse gesture transitions and movement dynamics across different users. This optimization process enables the Markov Chain to better generalize subject-independent sign language recognition, leading to improved classification accuracy, robustness against signer variability, and reduced misclassification rates. Experimental evaluations demonstrate a significant improvement in recognition performance, reduced error rates, and enhanced generalization across unseen signers, validating the effectiveness of the proposed approach.

Open Access: Yes

DOI: 10.3390/ai6080189

Type-2 Neutrosophic Markov Chain Model for Subject-Independent Sign Language Recognition: A New Uncertainty–Aware Soft Sensor Paradigm

Á Ballagi Oday Ali Hassen Saad M. Saad Muslem Al-Saidi

Publication Name: Sensors

Publication Date: 2024-12-01

Volume: 24

Issue: 23

Page Range: Unknown

Description:

Uncertainty-aware soft sensors in sign language recognition (SLR) integrate methods to quantify and manage the uncertainty in their predictions. This is particularly crucial in SLR due to the variability in sign language gestures and differences in individual signing styles. Managing uncertainty allows the system to handle variations in signing styles, lighting conditions, and occlusions more effectively. While current techniques for handling uncertainty in SLR systems offer significant benefits in terms of improved accuracy and robustness, they also come with notable disadvantages. High computational complexity, data dependency, scalability issues, sensor and environmental limitations, and real-time constraints all pose significant hurdles. The aim of the work is to develop and evaluate a Type-2 Neutrosophic Hidden Markov Model (HMM) for SLR that leverages the advanced uncertainty handling capabilities of Type-2 neutrosophic sets. In the suggested soft sensor model, the Foot of Uncertainty (FOU) allows Type-2 Neutrosophic HMMs to represent uncertainty as intervals, capturing the range of possible values for truth, falsity, and indeterminacy. This is especially useful in SLR, where gestures can be ambiguous or imprecise. This enhances the model’s ability to manage complex uncertainties in sign language gestures and mitigate issues related to model drift. The FOU provides a measure of confidence for each recognition result by indicating the range of uncertainty. By effectively addressing uncertainty and enhancing subject independence, the model can be integrated into real-life applications, improving interactions, learning, and accessibility for the hearing-impaired. Examples such as assistive devices, educational tools, and customer service automation highlight its transformative potential. The experimental evaluation demonstrates the superiority of the Type-2 Neutrosophic HMM over the Type-1 Neutrosophic HMM in terms of accuracy for SLR. Specifically, the Type-2 Neutrosophic HMM consistently outperforms its Type-1 counterpart across various test scenarios, achieving an average accuracy improvement of 10%.

Open Access: Yes

DOI: 10.3390/s24237828

Transfer Learning-Based Steering Angle Prediction and Control with Fuzzy Signatures-Enhanced Fuzzy Systems for Autonomous Vehicles

Á Ballagi Ahmet Mehmet Karadeniz L. T. Koczy

Publication Name: Symmetry

Publication Date: 2024-09-01

Volume: 16

Issue: 9

Page Range: Unknown

Description:

This research introduces an innovative approach for End-to-End steering angle prediction and its control in electric power steering (EPS) systems. The methodology integrates transfer learning-based computer vision techniques for prediction and control with fuzzy signatures-enhanced fuzzy systems. Fuzzy signatures are unique multidimensional data structures that represent data symbolically. This enhancement enables the fuzzy systems to effectively manage the inherent imprecision and uncertainty in various driving scenarios. The ultimate goal of this work is to assess the efficiency and performance of this combined approach by highlighting the pivotal role of steering angle prediction and control in the field of autonomous driving systems. Specifically, within EPS systems, the control of the motor directly influences the vehicle’s path and maneuverability. A significant breakthrough of this study is the successful application of transfer learning-based computer vision techniques to extract respective visual data without the need for large datasets. This represents an advancement in reducing the extensive data collection and computational load typically required. The findings of this research reveal the potential of this approach within EPS systems, with an MSE score of 0.0386 against 0.0476, by outperforming the existing NVIDIA model. This result provides a 22.63% better Mean Squared Error (MSE) score than NVIDIA’s model. The proposed model also showed better performance compared with all other three references found in the literature. Furthermore, we identify potential areas for refinement, such as decreasing model loss and simplifying the complex decision model of fuzzy systems, which can represent the symmetry and asymmetry of human decision-making systems. This study, therefore, contributes significantly to the ongoing evolution of autonomous driving systems.

Open Access: Yes

DOI: 10.3390/sym16091180

Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks

Á Ballagi Szabolcs Fischer János Hollósi Gábor Kovács Viktor Nagy

Publication Name: Computers

Publication Date: 2024-09-01

Volume: 13

Issue: 9

Page Range: Unknown

Description:

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications.

Open Access: Yes

DOI: 10.3390/computers13090218

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Á Ballagi Oday Ali Hassen Saad M. Saad Muslem Al-Saidi

Publication Name: Computers

Publication Date: 2024-04-01

Volume: 13

Issue: 4

Page Range: Unknown

Description:

In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

Open Access: Yes

DOI: 10.3390/computers13040106

Bus Driver Head Position Detection Using Capsule Networks under Dynamic Driving Conditions

Á Ballagi Szabolcs Fischer János Hollósi Gábor Kovács Viktor Nagy

Publication Name: Computers

Publication Date: 2024-03-01

Volume: 13

Issue: 3

Page Range: Unknown

Description:

Monitoring bus driver behavior and posture in urban public transport’s dynamic and unpredictable environment requires robust real-time analytics systems. Traditional camera-based systems that use computer vision techniques for facial recognition are foundational. However, they often struggle with real-world challenges such as sudden driver movements, active driver–passenger interactions, variations in lighting, and physical obstructions. Our investigation covers four different neural network architectures, including two variations of convolutional neural networks (CNNs) that form the comparative baseline. The capsule network (CapsNet) developed by our team has been shown to be superior in terms of efficiency and speed in facial recognition tasks compared to traditional models. It offers a new approach for rapidly and accurately detecting a driver’s head position within the wide-angled view of the bus driver’s cabin. This research demonstrates the potential of CapsNets in driver head and face detection and lays the foundation for integrating CapsNet-based solutions into real-time monitoring systems to enhance public transportation safety protocols.

Open Access: Yes

DOI: 10.3390/computers13030066

Investigation of System Stability and the Design of a Controller based on the Transfer Function of a Quadcopter’s BLDC Motor

Miklós Kuczmann Á Ballagi Barnabás Kiss

Publication Name: Chemical Engineering Transactions

Publication Date: 2024-01-01

Volume: 114

Issue: Unknown

Page Range: 751-756

Description:

The objective of this study is the control technology of quadcopters. The aim of this article is to propose further simulation assessment opportunities and other control implementations for investigating the transfer function of a quadrotor BLDC (Brushless Direct Current Electric Motor) motor obtained from experimental results in a previously published paper by separate authors. In this article, an LQ (linear-quadratic) controller is implemented based on the transmission function, during which the response of the controller to a unit step signal is examined. It is proved that LQ control can significantly enhance the autonomy of UAVs (Unmanned Aerial Vehicles) compared to PID (Proportional-Integral-Derivative Controller) control, as a faster and more accurate step response is achieved during system analysis. Additionally, how the LQ controller and the PID controller respond to a randomly generated white noise is examined. The results are compared with those implemented with a PID controller presented in a separate article.

Open Access: Yes

DOI: 10.3303/CET24114126

Assessment of SLAM Methods Applied in Monochromatic Environments †

Á Ballagi Rudolf Krecht

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

One of the most significant challenges in sustainable autonomous mobile robot and vehicle development is the perception of stochastic environments. Various environmental perception methods have been proposed to address these challenges; however, these methods often lack general applicability. Many of these methods rely on environmental feature extraction, which can fail in specific scenarios, such as monochromatic environments. This article aims to evaluate existing SLAM (Simultaneous Localization and Mapping) methods that utilize camera or combined camera and LiDAR input data in predominantly monochromatic environments. Additionally, this study seeks to identify performance issues in such applications.

Open Access: Yes

DOI: 10.3390/engproc2024079051

Exploration Techniques in Reinforcement Learning for Autonomous Vehicles †

Á Ballagi Ammar Khaleel

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

Autonomous vehicles (AVs) have the potential to revolutionize the transportation system by enhancing road safety, reducing traffic congestion, and freeing drivers from monotonous tasks. Effective exploration is essential for AVs to navigate safely and adapt to dynamic environments. Reinforcement learning (RL) enables AVs to learn optimal behaviors through continuous interaction with their environment. This paper reviews recent RL research on designing exploration strategies for single- and multi-agent AV systems. It categorizes exploration methods based on underlying principles and addresses the challenges. It analyzes key RL algorithms’ strengths, limitations, and empirical performance. By compiling and analyzing the current state of research, this paper aims to facilitate future advancements in AV exploration using RL, offering insights into current trends and future directions in this evolving field.

Open Access: Yes

DOI: 10.3390/engproc2024079024

Overview Study of the Applications of Unmanned Aerial Vehicles in the Transportation Sector †

Miklós Kuczmann Á Ballagi Barnabás Kiss

Publication Name: Engineering Proceedings

Publication Date: 2024-01-01

Volume: 79

Issue: 1

Page Range: Unknown

Description:

This study examines the use of Unmanned Aerial Vehicles (UAVs) in transportation, focusing on traffic monitoring and accident prevention. UAVs provide a cost-effective means for traffic surveillance, route planning, and accident analysis, enhancing data accuracy and timeliness. The paper discusses autonomous and human-intervention-supported drone systems for traffic surveillance, addressing technological and operational challenges and the balance needed for practical implementation. It also presents recent advancements, including a forerunner drone model, and references research on UAVs for maritime navigation safety, underscoring the need for their safe and efficient integration into transportation systems.

Open Access: Yes

DOI: 10.3390/engproc2024079011

Hamiltonian-Based Control Approach with Pendulum Application

R. C. Pozna Á Ballagi Radu Emil Precup

Publication Name: Saci 2024 18th IEEE International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 593-598

Description:

The paper proposes a design method dedicated to control systems, which use a certain energy function (surface), called the Hamiltonian of the system. The plant requires, most of the time, the convergence at different fixed points and at the same time in different ways (different behaviors). This phenomenon is possible by modifying the initial energy surface. Modification of the energy surface can be achieved by external (generalized) forces. The formalism suggested in this paper allows the calculation of generalized forces and, finally, to obtain the mentioned changes.

Open Access: Yes

DOI: 10.1109/SACI60582.2024.10619844

Using Tensor-Type Formalism in Causal Networks

R. C. Pozna Á Ballagi Radu Emil Precup

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2024-01-01

Volume: 21

Issue: 10

Page Range: 75-91

Description:

The causal network is a possible description of complex phenomena, and several domains, for example, Machine Learning, Social Science, and Artificial Intelligence. Although a successful solution is referred to in this paper, the field inherently faces challenges. Among these, the work identified that the formalism used is time-consuming and difficult to understand. Consequently, the approach proposed in this paper consists in transcribing this formalism in a tensor form. This goal is accomplished in three steps: first common tensor formulas are proposed for direct and inverse models; second these formulas are adapted for the network primitives; in the end the primitive and consequently the formula composition is analysed. To facilitate the understanding of the proposed formalism, the paper describes several examples. This paper is dedicated to Prof. Imre J. Rudas, to celebrate his 75th anniversary.

Open Access: Yes

DOI: 10.12700/APH.21.10.2024.10.5

Face Detection Using a Capsule Network for Driver Monitoring Application

Á Ballagi Szabolcs Fischer János Hollósi Gábor Kovács Viktor Nagy

Publication Name: Computers

Publication Date: 2023-08-01

Volume: 12

Issue: 8

Page Range: Unknown

Description:

Bus driver distraction and cognitive load lead to higher accident risk. Driver distraction sources and complex physical and psychological effects must be recognized and analyzed in real-world driving conditions to reduce risk and enhance overall road safety. The implementation of a camera-based system utilizing computer vision for face recognition emerges as a highly viable and effective driver monitoring approach applicable in public transport. Reliable, accurate, and unnoticeable software solutions need to be developed to reach the appropriate robustness of the system. The reliability of data recording depends mainly on external factors, such as vibration, camera lens contamination, lighting conditions, and other optical performance degradations. The current study introduces Capsule Networks (CapsNets) for image processing and face detection tasks. The authors’ goal is to create a fast and accurate system compared to state-of-the-art Neural Network (NN) algorithms. Based on the seven tests completed, the authors’ solution outperformed the other networks in terms of performance degradation in six out of seven cases. The results show that the applied capsule-based solution performs well, and the degradation in efficiency is noticeably smaller than for the presented convolutional neural networks when adversarial attack methods are used. From an application standpoint, ensuring the security and effectiveness of an image-based driver monitoring system relies heavily on the mitigation of disruptive occurrences, commonly referred to as “image distractions,” which represent attacks on the neural network.

Open Access: Yes

DOI: 10.3390/computers12080161

Simplified Routing Mechanism for Capsule Networks

R. C. Pozna Á Ballagi János Hollósi

Publication Name: Algorithms

Publication Date: 2023-07-01

Volume: 16

Issue: 7

Page Range: Unknown

Description:

Classifying digital images using neural networks is one of the most fundamental tasks within the field of artificial intelligence. For a long time, convolutional neural networks have proven to be the most efficient solution for processing visual data, such as classification, detection, or segmentation. The efficient operation of convolutional neural networks requires the use of data augmentation and a high number of feature maps to embed object transformations. Especially for large datasets, this approach is not very efficient. In 2017, Geoffrey Hinton and his research team introduced the theory of capsule networks. Capsule networks offer a solution to the problems of convolutional neural networks. In this approach, sufficient efficiency can be achieved without large-scale data augmentation. However, the training time for Hinton’s capsule network is much longer than for convolutional neural networks. We have examined the capsule networks and propose a modification in the routing mechanism to speed up the algorithm. This could reduce the training time of capsule networks by almost half in some cases. Moreover, our solution achieves performance improvements in the field of image classification.

Open Access: Yes

DOI: 10.3390/a16070336

Towards the resilience quantification of (military) unmanned ground vehicles

Á Ballagi Árpád Tóth Rudolf Krecht Alex Suta

Publication Name: Cleaner Engineering and Technology

Publication Date: 2023-06-01

Volume: 14

Issue: Unknown

Page Range: Unknown

Description:

In the case of Unmanned Ground Vehicles (UGVs), resilience can be an economical, an environmental, but most importantly, a mission-critical question as well: mission failure caused by the lack of resilience in some cases might imply the loss of the UGV, which could lead to human and financial losses and environmental damage. Thus, the aim of this article is to provide a methodology for UGV resilience analysis by introducing a generalizable method that can be applied both for complete UGV systems and subsystems, and leads to resilience quantification. After proposing a specific resilience definition for UGVs, this article proposes a method for UGV resilience assessment using process graphs, created based on the system components and the expected behavior of UGVs. To provide a context for the introduced solution, existing methods applied for UGV resilience assessment are briefly mentioned. The application of the proposed method is showcased on the perception subsystem of a UGV, finalized with the evaluation of the achieved results.

Open Access: Yes

DOI: 10.1016/j.clet.2023.100644

Capsule-based Autoencoder Network for Pointcloud Reconstruction

R. C. Pozna Á Ballagi János Hollósi

Publication Name: 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics Sami 2023 Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: 121-126

Description:

The theory of capsule networks and the dynamic routing mechanism for capsules was introduced by Geoffrey Hinton and his research team. In this new approach, they tried to solve typical problems of classical convolutional neural networks. For example, that the efficiency of neural networks degrades when a geometric transformation is applied on the input image, or when the data is far away from the training dataset. It became clear early on that capsule networks are state-of-the-art solutions for visual data classification tasks. For other tasks their use is less common and in many cases difficult to apply. For example image segmentation or object detection and localization. The efficiency of the capsule networks theory in the field of pointcloud processing is also an open question. In this work we investigated the pointcloud reconstruction capability of capsule networks. In this approach, three different complexity autoencoder networks was selected. We created a decoder network based on capsules theory, which was fitted to the existing autoencoder networks. The efficiency of the networks was tested using four different datasets. As a result of our work, we show the effectiveness of capsule networks in the field of pointcloud reconstruction compared with the selected autoencoder networks.

Open Access: Yes

DOI: 10.1109/SAMI58000.2023.10044532

Capsule Network based 3D Object Orientation Estimation

R. C. Pozna Á Ballagi János Hollósi

Publication Name: International Conference on Electrical Computer Communications and Mechatronics Engineering Iceccme 2023

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Convolutional neural networks have proven to be one of the most efficient methods for processing visual data. Due to the popularity of the field, there is a growing interest in the reliability of intelligent systems. It has been shown that convolutional neural networks can be fooled by extreme inputs or noisy inputs. To overcome the current problems of convolutional neural networks, the theory of capsule networks was introduced by Geoffrey Hinton and his research team. In this work we want to investigate the theory of capsule networks for orientation recognition of 3-dimensional objects. We consider the case when the data are noise loaded by various adversarial attacking methods. We compare our results with the efficiency of convolutional neural network based solutions, highlighting the difference between the two theories. We investigate the efficiency reduction that can be observed using different adversarial attacking methods. Our results will show how much more efficient the capsule network is compared to the neural networks.

Open Access: Yes

DOI: 10.1109/ICECCME57830.2023.10252762

Validation Process of the Computer Simulation of a Test-Purpose Self-Driving Vehicle

Á Ballagi Miklos Unger Rudolf Krecht

Publication Name: Iavvc 2023 IEEE International Automated Vehicle Validation Conference Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The aim of this article is to propose the initial validation steps for a test-purpose self-driving vehicle. The basis of the computer simulation in question is a passenger vehicle converted to be capable of specific self-driving tasks, meaning that it features a complete low-level control system, an onboard computer for high-level computation and a full sensor set consisting of laser scanners, lidars, cameras and GNSS receivers. The computer simulation of the described vehicle is created using the SVL Autonomous Vehicle Simulator and aims to completely model the behavior of the real transformed vehicle. To ensure the fidelity of the computer simulation, a set of comparative measurements are defined, which are realized using both the real vehicle and its computer simulation. The basis of comparison is, on one hand, the assessment of the vehicle control system by comparing control input and output, on the other hand, the comparison of onboard sensor measurement results.

Open Access: Yes

DOI: 10.1109/IAVVC57316.2023.10328107

Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems

Á Ballagi Norbert Marko Tamas Sziranyi

Publication Name: Iavvc 2023 IEEE International Automated Vehicle Validation Conference Proceedings

Publication Date: 2023-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The prediction of terrain elevation values is a key task when it comes to off-road dynamics and inertial data estimation. A reliable elevation map can help in the estimation of future vehicle states and thus extend the response time window for autonomous navigation and control. We trained a deep learning model that is able to successfully predict top-down terrain depth maps in an off-road setting using a lightweight monocular depth estimation network. The labels were generated using a custom preprocessing algorithm to aid single image depth model training. Unlike other elevation estimation algorithms, our work can predict terrain variation from a higher camera setting without the use of a multi-sensor system. The network is also shown to work outside of the training data domain. The code will be available at https://www.github.com/norbertmarko/terrain-depth.

Open Access: Yes

DOI: 10.1109/IAVVC57316.2023.10328139

Digital Twin of Drone-based Protection of Agricultural Areas

Á Ballagi Csaba Hajdu A. J. Kovács János Hollósi Gergely Teschner Norbert Boros

Publication Name: 2022 IEEE 1st International Conference on Internet of Digital Reality Iod 2022

Publication Date: 2022-01-01

Volume: Unknown

Issue: Unknown

Page Range: 99-104

Description:

Protecting agricultural fields, like crops, vineyards, and husbandry areas, has been a difficult challenge since historical times. Classical methods to prevent intrusion are often destructive to wild and domestic animals alike. Even more current nondestructive systems, like camera-based systems are attributed to specific problems related to environmental or technological issues. Furthermore, verifying the effectiveness of installed systems is difficult, as the triggering situations are unmanageable and typically occur unsupervised. This paper presents a complex vision-based intrusion detection system to overcome these problems and further proposes more extensive control and flexibility on the development process. The solution provides a workflow integrating Digital Reality methods into the system development by creating a digital twin of the drone and its surrounding environment in a general-purpose robotic simulator. With this simulation, the triggering events and environmental effects can be easily emulated, such as a wild animal entering the area of interest. The solution also focuses on incorporating new 5G info-communication networks on handling communication between the intrusion detection system and the base station in a distributed manner.

Open Access: Yes

DOI: 10.1109/IoD55468.2022.9986763

EDLRIS: A European Driving License for Robots and Intelligent Systems

R. C. Pozna Á Ballagi Imre Szalay Marlene Miglbauer Martin Kandlhofer Ronald Bieber Wilfried Baumann Istvan Alfoldi Manuel Menzinger Sandra Plomer Margit Ehardt-Schmiederer Inge Strobl-Zuchtriegl Gabor Miltenyi Thomas Winkler Julia Lassnig Gerald Steinbauer

Publication Name: Ki Kunstliche Intelligenz

Publication Date: 2021-06-01

Volume: 35

Issue: 2

Page Range: 221-232

Description:

This article presents a novel educational project aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying teachers, school students and young people in Artificial Intelligence (AI) and Robotics. In recent years, AI and Robotics have become major topics with a huge impact not only on our everyday life but also on the working environment. Hence, sound knowledge about principles and concepts of AI and Robotics are key skills for this century. Nonetheless, hardly any systematic approaches exist that focus on teaching principles of intelligent systems at K-12 level, addressing students as well as teachers who act as multipliers. In order to meet this challenge, the European Driving License for Robots and Intelligent Systems—EDLRIS was developed. It is based on a number of previously implemented and evaluated projects and comprises teaching curricula and training modules for AI and Robotics, following a competency-based, blended learning approach. Additionally, a certification system proves peoples’ acquired competencies. After developing the training and certification system, the first 32 trainer and trainee courses with a total of 445 participants have been implemented and evaluated. By applying this innovative approach—a standardized and widely recognized training and certification system for AI and Robotics at K-12 level for both high school teachers and students—we envision to foster AI/Robotics literacy on a broad basis.

Open Access: Yes

DOI: 10.1007/s13218-021-00716-8

Robot environment representation based on Quadtree organization of Fuzzy Signatures

Á Ballagi Csaba Hajdu Ahmet Mehmet Karadeniz L. T. Koczy

Publication Name: Saci 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2021-05-19

Volume: Unknown

Issue: Unknown

Page Range: 509-514

Description:

This paper presents a novel approach to mobile robot environment representation to hold information on detected obstacles. The method is inspired by fuzzy signature-based formalism and is based on classical quadtrees as a data indexing structure. Each detected feature point is evaluated by a fuzzy-ruleset defining the presumed significance of each detected object. Feature points and their fuzzy-mapping are indexed in a classical quadtree-based fashion. During the reconstruction of the environment representation, inference is done by the traversal on the constructed tree using accumulated fuzzy-ruleset. Our goal is to use this representation format for further robotic tasks such as obstacle avoidance in a distributed computational environment.

Open Access: Yes

DOI: 10.1109/SACI51354.2021.9465566

Possible Control Methods for Skid-Steer Mobile Robot Platforms

Á Ballagi Csaba Hajdu Rudolf Krecht

Publication Name: Gpmc 2020 2nd IEEE International Conference on Gridding and Polytope Based Modeling and Control Proceedings

Publication Date: 2020-11-19

Volume: Unknown

Issue: Unknown

Page Range: 31-34

Description:

The aim of this paper is to collect and analyze the possible low-level control methods for skid-steer mobile robot platforms. Most low-level control methods for mobile robots rely on the kinematic or dynamic model of the controlled robot platform. However, in case of skid-steer mobile robots, a precise kinematic or dynamic model might be too costly to be implemented for a control method. Thus, it has an outstanding importance to find a control method which can be precise enough even with a simplified kinematic or dynamic model. The overall control scheme of skid-steer robots is also discussed, along with issues regarding the kinematic and dynamic modelling of the mentioned platforms.

Open Access: Yes

DOI: 10.1109/GPMC50267.2020.9333814

Towards a quadtree based approach to learn local plans in robotic motion planning

Á Ballagi Csaba Hajdu

Publication Name: Gpmc 2020 2nd IEEE International Conference on Gridding and Polytope Based Modeling and Control Proceedings

Publication Date: 2020-11-19

Volume: Unknown

Issue: Unknown

Page Range: 25-30

Description:

In this paper, a novel approach to the local planning of mobile robots and autonomous vehicles is discussed. This paper introduces a motion planning architecture utilizing both a conventional (Hybrid A∗) and a learning-based planner, inspired by the recent results of reinforcement learning. The presented approach relies on a grid-based representation of the environment which is simultaneously used for planning and learning of such trajectories. The representation grid is derived from a quadtree representation of the environment and the definition is extended with convex polytopic description, to produce grid-based and Voronoi diagrams. The paper also discusses the possible integration of more sophisticated soft-computing-based control, like TP-model transformation as a basis for the heuristics used by motion planning components.

Open Access: Yes

DOI: 10.1109/GPMC50267.2020.9333811

Economical Mobile Robot Design Prototype and Simulation for Industry 4.0 Applications

R. C. Pozna Á Ballagi Csaba Hajdu János Hollósi Rudolf Krecht

Publication Name: Cando EPE 2020 Proceedings IEEE 3rd International Conference and Workshop in Obuda on Electrical and Power Engineering

Publication Date: 2020-11-18

Volume: Unknown

Issue: Unknown

Page Range: 155-160

Description:

Autonomous mobile robots received a rising research interest, with the appearance of complex cyber-physical system applications, like Industry 4.0. Related scenarios require not only some degree of autonomous capabilities of the control software but also tight interaction between humans as well. In this paper, a prototype autonomous mobile robot of such functionalities is presented with additionally aiming cost-effective construction. The robot is currently capable of performing basic tasks, like traversing to a goal configuration, mapping the environment, and detecting and avoiding obstacles. To allow the versatile extension with additional cyber-physical software, the control software interfaces the Robot Operating System (ROS), a popular framework in robotic research. During the development of software and hardware, simulation has been heavily utilized. This allowed an iterative development and the use of verification in the early development phases.

Open Access: Yes

DOI: 10.1109/CANDO-EPE51100.2020.9337786

Proposal of a graph-based motion planner architecture

Á Ballagi Csaba Hajdu

Publication Name: 11th IEEE International Conference on Cognitive Infocommunications Coginfocom 2020 Proceedings

Publication Date: 2020-09-23

Volume: Unknown

Issue: Unknown

Page Range: 393-398

Description:

Motion planning is a critical task in robots and autonomous vehicles. This task is typically complex in computation and structure. Current frameworks are difficult to extend and supervise. This article presents a new architectural proposal of motion planning software, which can be used by mobile robots and autonomous vehicles. Defining aspects have been inspired by the foundations of Cognitive Infocommunications (CogInfoCom) to construct a highly interactive system aiming intuitive human-computer collaboration. The structure is based on a graph-based approach which is suitable in a distributed setting. This formalization provides a graphical behavior description, an intuitive way for human actors to interact with the system. The behavior of each acting component is based on a hybrid-system formalism which can further serve as a basis to model human behavior.

Open Access: Yes

DOI: 10.1109/CogInfoCom50765.2020.9237891

Model-oriented control software development of academic autonomous test vehicle

Á Ballagi Csaba Hajdu

Publication Name: Ines 2020 IEEE 24th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2020-07-01

Volume: Unknown

Issue: Unknown

Page Range: 73-78

Description:

This paper presents a model-oriented toolset developed for academic autonomous vehicle projects. Typical parameters are modeled into a structured domain model, which provides an organized view of the target vehicle. This model can be used to generate the bridge software linking low-level control software (e.g. CAN network) with high-level middleware frameworks. Other software items can be also originated from this model, including the configuration of deployed sensory components and simulation description. Code generation is performed via transforming the vehicle domain model instance into other specific domains (e.g. general kinematic description from vehicle description). As a result, a tool is provided which can be used to define a vehicle configuration in a textual format. The generators create code interfacing the open-source Robot Operating System (ROS).

Open Access: Yes

DOI: 10.1109/INES49302.2020.9147202

Training Neural Networks with Computer Generated Images

Á Ballagi János Hollósi

Publication Name: Informatics 2019 IEEE 15th International Scientific Conference on Informatics Proceedings

Publication Date: 2019-11-01

Volume: Unknown

Issue: Unknown

Page Range: 155-159

Description:

At the Széchenyi István University we develop an autonomous racing car for the Shell Eco-marathon. One of the main tasks is to create a neural network which is segment the road surface, the protective barriers and other components of the race track. The difficulty of this task, that there is no a right dataset for this special issue. Only a limited size dataset available, therefore, we would like to expands this dataset with computer generated training images, which comes from a virtual city environment. In this work we want to examine the effect of computer generated images on the efficiency of different neural networks. In the training process real images and computer generated virtual images are mixed in several different ways. After that, three different neural network architecture for road surface and road barrier detection are trained. Experiences shows how to mixing datasets and how they can improve efficiency.

Open Access: Yes

DOI: 10.1109/Informatics47936.2019.9119273

Enabling the Creation of Intelligent Things: Bringing Artificial Intelligence and Robotics to Schools

Á Ballagi Martin Kandlhofer Wilfried Baumann Istvan Alfoldi Julia P. Lasnig Sandra Plomer Gerald Steinbauer

Publication Name: Proceedings Frontiers in Education Conference Fie

Publication Date: 2019-10-01

Volume: 2019-October

Issue: Unknown

Page Range: Unknown

Description:

This Innovative Practice Work in Progress paper presents a novel educational project, aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying teachers, school students and young people in Artificial Intelligence (AI) and Robotics. In recent years, AI and Robotics have become major topics with a huge impact not only on our everyday life but also on the working environment. Hence, sound knowledge about principles and concepts of AI and Robotics are key skills for the 21st century. Nonetheless, hardly any systematic approaches exist that focus on teaching AI/Robotics principles at K-12 level, addressing both teachers and students. In order to meet this challenge, the European Driving License for Robots and Intelligent Systems is under development. It is based on a number of previously implemented and evaluated projects and comprises teaching curricula and training modules for AI/Robotics, following a competency based, blended learning approach. Additionally, a certification system proves peoples' competencies acquired during the training. By applying this innovative approach - a standardized and widely recognized training and certification system for AI and Robotics at K-12 level for both teachers and students - we envision to foster AI/Robotics literacy on a broad basis.

Open Access: Yes

DOI: 10.1109/FIE43999.2019.9028537

Training Capsule Networks with Various Parameters

Á Ballagi János Hollósi

Publication Name: Saci 2019 IEEE 13th International Symposium on Applied Computational Intelligence and Informatics Proceedings

Publication Date: 2019-05-01

Volume: Unknown

Issue: Unknown

Page Range: 191-196

Description:

Nowadays convolutional neural networks (CNNs) have produced the state-of-The-Art performance in image classification and segmentation tasks. The efficiency of the neural networks is one of the bests when the testing samples are close to the training data. Nevertheless, if we make some transformation on the dataset, the performance of the convolutional neural network reduced. Recently, capsule networks (CapsNet) have been introduced to solve some of the problems of neural networks. In this paper we examine the effectiveness of three different capsule based neural networks, and compare the performance when the parameters of the dynamic routing algorithm and the squashing function are modified.

Open Access: Yes

DOI: 10.1109/SACI46893.2019.9111574

Range sensor-based occupancy grid mapping with signatures

Á Ballagi Csaba Hajdu Ernö Horváth Claudiu Radu

Publication Name: Proceedings of the 2019 20th International Carpathian Control Conference Iccc 2019

Publication Date: 2019-05-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Recently autonomous navigation technologies have received huge attention in many fields, such as autonomous driving or mobile robotics. Simultaneous localization and mapping (SLAM) [1] is a key problem in this field and the current paper targets the mapping part of it. Mapping means to construct accurate static or even dynamic representation of the observable environment. There are of course solutions for range sensor-based mapping, but the current paper proposes a new approach, which involves signatures. Signature is a mathematical data representation, which handles well uncertainty in complex systems.

Open Access: Yes

DOI: 10.1109/CarpathianCC.2019.8765684

A Mobile Robot and Vehicle Occupancy Map Construction Model

R. C. Pozna Á Ballagi Ernö Horváth

Publication Name: Ines 2019 IEEE 23rd International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2019-04-01

Volume: Unknown

Issue: Unknown

Page Range: 59-64

Description:

Occupancy gird map is a popular way of representing the environment regarding robots and vehicles. It consists of discrete cells, where each occupancy grid cell is valued independently. Because the represented grid structure is rigid it requires priori known memory resource concerning the size of the environment to obtain. Since their introduction, occupancy grid maps are still researched intensively [1] [2] [3]. In the current paper we propose the mathematical description of the computational simplification via alternative methods for occupancy map.

Open Access: Yes

DOI: 10.1109/INES46365.2019.9109531

Evaluation of Neural Network-Based Sensing and Perception in Experimental Vehicles

R. C. Pozna Á Ballagi Ernö Horváth

Publication Name: Ines 2018 IEEE 22nd International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2018-11-05

Volume: Unknown

Issue: Unknown

Page Range: 000113-000118

Description:

As increasing use of neural networks (NN) and deep-learning (DL) can be observed in various fields, such as voice and image recognition, control, translation, data processing, function approximation, etc. These technologies can also be applied in a specific domain of experimental vehicles. This paper presents and summarizes our recent practical experiences gained during the usage of neural network as sensing and perception subsystem in an experimental vehicle. The paper does not targets general NN problems, only focuses on the narrow subdomain. The current paper also reviews and evaluates the available hyper parameter tunings, optimizers and neural network architectures and proposes tested solutions for enhanced performance. All of the used datasets, source codes and additional materials is available online.

Open Access: Yes

DOI: 10.1109/INES.2018.8523952

Fuzzy signature based description of complex overtaking and engagement conflicts in railway traffic control

Gábor Rózsa Á Ballagi L. T. Koczy

Publication Name: IEEE AFRICON Conference

Publication Date: 2015-11-18

Volume: 2015-November

Issue: Unknown

Page Range: Unknown

Description:

The railway traffic system and its respective subsystems, especially station and timetable management are extremely large and complex systems comprising a multitude of complex structured data. The theoretical and pre-planned traffic control is calculating with idealized traffic situations, but the real life scenes often suffer from conflicts and deviations. The complex traffic conflict states include uncertain conditions and vagueness of information. Classical methods cannot work properly under these uncertain conditions. In this paper we propose a new approach to describe these complex conflict situations using fuzzy signatures for recognizing and describing traffic conflicts in a hierarchically structured manner.

Open Access: Yes

DOI: 10.1109/AFRCON.2015.7331929

A new fuzzy graph and signature based approach to describe fuzzy situational maps

R. C. Pozna Á Ballagi L. T. Koczy

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2014-09-04

Volume: Unknown

Issue: Unknown

Page Range: 1340-1345

Description:

Computational tasks involving intelligent agents often need to process complex structured information. The way of describing this information greatly influences the performance of the agent. Therefore, a big issue is how the complex data describing that valuable information is not lose while it can also be processed in tractable time. Fuzzy signatures and their multidimensional geometric extension, fuzzy situational maps, are used to describe such complex structured data. These problems are examined in the context of a cooperative mobile robot task and a new method is developed for the simplified describing and processing of the complex inner relations in fuzzy situational maps. This paper mainly deals with the fundamentals of this method.

Open Access: Yes

DOI: 10.1109/FUZZ-IEEE.2014.6891862

Intelligent robot cooperation with fuzzy communication

R. C. Pozna Á Ballagi L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2014-02-03

Volume: 530

Issue: Unknown

Page Range: 185-197

Description:

Designing the decision-making engine of a robot which works in a collaborative team is a challenging task. This is not only due to the complexity of the environment uncertainty, dynamism and imprecision, but also because of the coordination of the team has to be included in this design. The robots must be aware of other robots' actions in order to cooperate and to successfully achieve their common goal. In addition, decisions must be made in real-time and using limited computational resources. In this chapter we propose some novel algorithms for action selection in ambiguous tasks where the communication opportunities among the robots are very limited. © Springer International Publishing Switzerland 2014.

Open Access: Yes

DOI: 10.1007/978-3-319-03206-1_14

Fuzzy Situational Maps: A new approach in mobile robot cooperation

R. C. Pozna Á Ballagi P. Földesi L. T. Koczy

Publication Name: Ines 2013 IEEE 17th International Conference on Intelligent Engineering Systems Proceedings

Publication Date: 2013-12-12

Volume: Unknown

Issue: Unknown

Page Range: 287-292

Description:

Intelligent robot cooperation tasks have very complex decision-making and computational processes. Collecting and calculating with a high amount of data is one of the weakest point of such system. In addition all of these it is necessary to process in real-time with limited computational capacity. In this paper we propose some novel algorithms for coping with these problems and give some information about the Fuzzy Situational Maps as a special case of the Fuzzy Signatures. An example takes to the field of warehouse logistics, managing and arranging boxes will be presented. © 2013 IEEE.

Open Access: Yes

DOI: 10.1109/INES.2013.6632828

Signatures: Definitions, operators and applications to fuzzy modelling

R. C. Pozna Á Ballagi Nicuşor Minculete Radu Emil Precup L. T. Koczy

Publication Name: Fuzzy Sets and Systems

Publication Date: 2012-08-16

Volume: 201

Issue: Unknown

Page Range: 86-104

Description:

This paper presents a new framework for the symbolic representation of data which is referred to as signatures. The definitions of signatures and of signature trees are first given. Original operators on signatures are next presented, i.e., contraction, extension, pruning, addition, multiplication, and grafting. Attractive applications of signatures related to the modelling of fuzzy inference systems are suggested and discussed. An example is included to accompany the theoretical results. © 2012 Elsevier B.V. All rights reserved.

Open Access: Yes

DOI: 10.1016/j.fss.2011.12.016

A cooperation scenario for multiagent systems

R. C. Pozna Á Ballagi Nicuşor Minculete Radu Emil Precup L. T. Koczy

Publication Name: IEEE AFRICON Conference

Publication Date: 2011-12-12

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper gives aspects related to a cooperation scenario in the framework of multiagent systems. The presentation is focused on a trivial multiagent system that consists of two agents, the Master and the Apprentice. The theoretical basis of the cooperation scenario is the definition of the most probable process, and two algorithms are used with this regard. The formulation of the cooperation scenario is exemplified for a case study that builds an architecture of successively placed bricks in the workspace. © 2011 IEEE.

Open Access: Yes

DOI: 10.1109/AFRCON.2011.6071961

Nonlinear systems controller design as a result of uninorm tuning

R. C. Pozna Márta Takács Á Ballagi L. T. Koczy

Publication Name: 2011 2nd International Conference on Cognitive Infocommunications Coginfocom 2011

Publication Date: 2011-09-23

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

The quality of a Fuzzy Logic Controller (FLC) for control nonlinear systems highly depends on the used operators. Using the uninorm operators in these processes the FLC components can be adapted to achieve better results. The program language environment, applied by simulation, supports the choosing of suitable fuzzy operators and their parameters. The simulation results are analyzed, depending on the efficiency of the operator choice in approximate reasoning model. © 2011 Scientific Assoc Infocomm.

Open Access: Yes

DOI: DOI not available

A kantian pattern of knowledge, the observation representation

R. C. Pozna Á Ballagi Radu Emil Precup L. T. Koczy

Publication Name: Siisy 2010 8th IEEE International Symposium on Intelligent Systems and Informatics

Publication Date: 2010-12-30

Volume: Unknown

Issue: Unknown

Page Range: 405-412

Description:

This paper suggests a new pattern of human knowledge. Its execution represents an important stage in the general framework of aiming the building of an artificial cognitive system. First the general presentation of the artificial cognitive system, its objective, main principles and implementation phases are highlighted. The known patterns, which have a cultural (philosophical, psychological and linguistic) origin are then analyzed. Finally the details concerning the proposed pattern are pointed out. ©2010 IEEE.

Open Access: Yes

DOI: 10.1109/SISY.2010.5647371

Fuzzy communication in collaboration of intelligent agents

Á Ballagi L. T. Koczy

Publication Name: Proceedings of the 9th Wseas International Conference on Applied Computer and Applied Computational Science Acacos 10

Publication Date: 2010-12-01

Volume: Unknown

Issue: Unknown

Page Range: 208-214

Description:

This paper presents some examples for fuzzy communication and intention guessing from the real life to the cooperation of intelligent mobile robots. In a special experimental environment a new communication approach is investigated for intelligent cooperation of autonomous mobile robots. Effective, fast and compact communication is one of the most important cornerstones of a high-end cooperating system. In this paper we propose a fuzzy communication system where the codebooks are built up by fuzzy signatures. We use cooperating autonomous mobile robots to solve some logistic problems.

Open Access: Yes

DOI: DOI not available

Context recognition in mobile robots cooperation using fuzzy signature

R. C. Pozna Á Ballagi L. T. Koczy

Publication Name: International Conference on Theoretical and Mathematical Foundations of Computer Science 2010 Tmfcs 2010

Publication Date: 2010-12-01

Volume: Unknown

Issue: Unknown

Page Range: 110-115

Description:

Robots cooperation means also to define the commune task. In order to understand their cooperation the robots must have complete knowledge about all possible states of the work or to understand the context by extract useful information from observation. Present paper focus on the second possibility. We propose a strategy of information extraction and context understanding based on an original data structure, the fuzzy signature and on a priori knowledge, the robot codebook. The paper starts by presenting the concept of the fuzzy signature and exemplify the idea of cooperation by context understanding.

Open Access: Yes

DOI: DOI not available

Man-machine cooperation without explicit communication

R. C. Pozna Á Ballagi L. T. Koczy

Publication Name: 2010 World Automation Congress Wac 2010

Publication Date: 2010-12-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper presents a novel method for control cooperation between human and robots without any explicit communication line. We have proposed a fuzzy communication philosophy and implementation technique, where the code books are built up by fuzzy signatures. Fuzzy signatures are used as complex state description method for intention guessing and action selection. Robots cooperation means also to define the commune task. In order to understand their cooperation the robots must have complete knowledge about all possible states of the work or to understand the context by extract useful information from observation. Present paper focus on the second possibility. We propose a strategy of information extraction and context understanding based on an original data structure, the fuzzy signature and on a priori knowledge, the robot codebook. The paper starts by presenting the concept of the fuzzy signature and exemplify the idea of cooperation by context understanding. © 2010 TSI Press.

Open Access: Yes

DOI: DOI not available

Fuzzy signature based fuzzy communication of mobile robots

R. C. Pozna Á Ballagi L. T. Koczy

Publication Name: 2010 IEEE Rivf International Conference on Computing and Communication Technologies Research Innovation and Vision for the Future Rivf 2010

Publication Date: 2010-12-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper presents a novel method for control cooperating robots where the explicit communication line is substituted with fuzzy communication algorithms. Fuzzy signatures are used in a context dependent codebook, as complex state description method for intention guessing and action selection. Finally a possible cooperative robot application on a realistic example with missing data components will be shown. ©2010 IEEE.

Open Access: Yes

DOI: 10.1109/RIVF.2010.5633066

Decision making in multi-robot cooperation by fuzzy signature sets

Á Ballagi L. T. Koczy

Publication Name: 2010 IEEE World Congress on Computational Intelligence Wcci 2010

Publication Date: 2010-11-25

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

This paper presents some examples for fuzzy communication and intention guessing from the real life to the cooperation of intelligent mobile robots. In a special experimental environment a new communication approach is investigated for intelligent cooperation of autonomous mobile robots. Effective, fast and compact communication is one of the most important cornerstones of a high-end cooperating system. In this paper we propose a fuzzy communication system where the codebooks are built up by fuzzy signatures. We use cooperating autonomous mobile robots to solve some logistic problems. © 2010 IEEE.

Open Access: Yes

DOI: 10.1109/FUZZY.2010.5584821

Robot cooperation by fuzzy signature sets rule base

Á Ballagi L. T. Koczy

Publication Name: Sami 2010 8th International Symposium on Applied Machine Intelligence and Informatics Proceedings

Publication Date: 2010-04-30

Volume: Unknown

Issue: Unknown

Page Range: 37-42

Description:

This paper presents a novel method for control cooperating robots without any explicit communication line. We have proposed a fuzzy communication philosophy and implementation technique, where the codebooks are built up by signatures. Fuzzy signatures are used as complex state description method for intention guessing and action selection. Finally some real scenarios of autonomous mobile robot cooperation are presented. ©2010 IEEE.

Open Access: Yes

DOI: 10.1109/SAMI.2010.5423703

Multi-robot cooperation by fuzzy signature sets

Á Ballagi L. T. Koczy

Publication Name: Imcic 2010 International Multi Conference on Complexity Informatics and Cybernetics Proceedings

Publication Date: 2010-01-01

Volume: 2

Issue: Unknown

Page Range: 154-159

Description:

This paper presents a novel method for control cooperating robots without any explicit communication line. Fuzzy signatures are used as complex state description method for intention guessing and action selection. Finally a possible cooperative robot application on a realistic example with missing data components will be shown.

Open Access: Yes

DOI: DOI not available

Motion control and communication of cooperating intelligent robots by fuzzy signatures

T. D. Gedeon Á Ballagi L. T. Koczy

Publication Name: IEEE International Conference on Fuzzy Systems

Publication Date: 2009-12-10

Volume: Unknown

Issue: Unknown

Page Range: 1073-1078

Description:

This paper presents two examples of usage of fuzzy signatures in the field of mobile robotics. The first shows a complex lateral drift control method base on fuzzy signatures. This method inspects the motion system of the robot as a whole, unlike as simple parts of a complex system. The state space is written down by fuzzy signatures which add up flexibility, adaptability and learning ability to the system. In the second experiment a new communication approach is investigated for intelligent cooperation of autonomous mobile robots. Effective, fast and compact communication is one of the most important cornerstones of a high-end cooperating system. In this paper we propose a fuzzy communication system where the codebooks are built up by fuzzy signatures. We use cooperating autonomous mobile robots to solve some logistic problems. ©2009 IEEE.

Open Access: Yes

DOI: 10.1109/FUZZY.2009.5277207

Robot cooperation without explicit communication by fuzzy signatures and decision trees

T. D. Gedeon Á Ballagi L. T. Koczy

Publication Name: 2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference Ifsa Eusflat 2009 Proceedings

Publication Date: 2009-12-01

Volume: Unknown

Issue: Unknown

Page Range: 1468-1473

Description:

This paper presents a novel action selection method for multi robot task sharing problem. Two autonomous mobile robots try to cooperate for push a box to a goal position. Both robots equipped with object and goal sensing, but do not have explicit communication ability. We explore the use of fuzzy signatures and decision making system to intention guessing and efficient action selection. Virtual reality simulation is used to build and test our proposed algorithm.

Open Access: Yes

DOI: DOI not available

Fuzzy communication and motion control by fuzzy signatures in intelligent mobile robots

T. D. Gedeon Á Ballagi L. T. Koczy

Publication Name: Studies in Computational Intelligence

Publication Date: 2009-10-26

Volume: 241

Issue: Unknown

Page Range: 147-164

Description:

This paper presents two examples for the deployment of fuzzy signatures in the field of intelligent mobile robots. The first shows a complex lateral drift control method base on fuzzy signatures. This method considers the motion system of the robot as a whole, unlike as simple parts of a complex system. The state space is written down by fuzzy signatures which add up flexibility, adaptability and learning ability to the system. In the second experiment a new communication approach is investigated for intelligent cooperation of autonomous mobile robots. Effective, fast and compact communication is one of the most important cornerstones of a high-end cooperating system. In this paper we propose a fuzzy communication system where the codebooks are built up by fuzzy signatures. We use cooperating autonomous mobile robots to solve some logistic problems. © 2009 Springer-Verlag Berlin Heidelberg.

Open Access: Yes

DOI: 10.1007/978-3-642-03633-0_9

Fuzzy signature based mobil robot motion control system

Á Ballagi L. T. Koczy

Publication Name: Sami 2008 6th International Symposium on Applied Machine Intelligence and Informatics Proceedings

Publication Date: 2008-08-25

Volume: Unknown

Issue: Unknown

Page Range: 29-33

Description:

The differentially steered drive system used in many robots is essentially the same arrangement as that used in a wheelchair. Thus, steering the robot is just a matter of varying the speeds of the drive wheels. At least two independent driving chain are used in most of differentially steered drive system. Each driver wheel has the own controller in a traditional motion system, which give a hard tuned, rigid arrangement. In this paper we propose a complex lateral drift control method base on fuzzy signatures. This method inspects the motion system as a whole, unlike as simple parts of a complex system. The state space is written down by fuzzy signatures which adds up flexibility, adaptability and learning ability to system. © 2008 IEEE.

Open Access: Yes

DOI: 10.1109/SAMI.2008.4469193

Exploring Fuzzy Signatures in Sensor Fusion: A Comparative Study with the Complementary Filter

Á Ballagi Ahmet Mehmet Karadeniz L. T. Koczy

Publication Name: Cinti 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 69-74

Description:

Sensing has become a pivotal element in the development of autonomous systems with the advancement of the technology. These systems operate on a sense-think-act cycle to execute tasks, necessitating the integration of multiple sensors. The challenge of synthesizing meaningful information from diverse data sources escalates with the complexity of the data. This study tackles the issue of sensor data complexity by investigating the potential of Fuzzy Signatures, which are promising in handling complex data due to their hierarchical structures. The main goal is to present a concept for sensor fusion based on Fuzzy Signatures, which may facilitate their use in autonomous system tasks. To demonstrate this concept, accelerometer and gyroscope data are utilized, with results compared to those from a Complementary Filter providing insight into the sensor fusion capabilities of Fuzzy Signatures. The study also underscores the importance of aggregation operators in Fuzzy Signatures, focusing on the Max and WRAO (weighted relevance aggregation operator) aggregation operators. The potential to employ various aggregation operators or to develop new ones for specific applications is highlighted. The findings indicate that Fuzzy Signatures could be an effective solution for sensor fusion challenges, offering prospects for enhancement and broader application in autonomous systems.

Open Access: Yes

DOI: 10.1109/CINTI63048.2024.10830837