Journal of Automation, Mobile Robotics and Intelligent Systems
Journal of Automation, Mobile Robotics and Intelligent Systems (JAMRIS) is an international scientific quarterly published in Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP located in Warsaw, Poland. JAMRIS publishes since 2007 high-quality technical, position and state-of-the-art papers in the fields of theory, design and applications of broadly perceived intelligent technologies, notably based on artificial intelligence and computational intelligence, particularly for analysing and solving scientific and engineering problems and systems. The articles are published in English, and they are peer-reviewed and open-access. ISSN: 1897-8649 (Print) | ISSN: 2080-2145 (Online) | DOI: 10.14313/JAMRIS/
Łukasiewicz – Industrial Research Institute for Automation and Measurements PIAP
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Copyright (c) 2025 Journal of Automation, Mobile Robotics and Intelligent Systems https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning–Driven Classification of Text-Based Cybercrime under the Indian IT ACT
Cybercrimes especially encompasses crime against children, data breaches, and privacy violations, are increasing in frequency due to the quick development of technology, which emphasizes the necessity of complex systems to analyse and categorize these offenses. There are many opportunities to analyze cybercrime data using Machine Learning (ML) techniques because of its enormous accumulation. This study proposes a model that has the potential to automatically analyze text-based reported cybercrime complaints based on the features by use of Random Forest (RF) and Gradient Boosting (GB) algorithms. This model includes a Bag of Words (BoW) approach for feature engineering to analyze reported cyber crime and suggest relevant Indian IT Act sections, such as Section 66E deals with privacy protection, Section 43A for reported data breach, and Section 72A for disclosure of information, using Natural Language Processing (NLP) for feature extraction and classification. This strategy enhanced the law and enforcement process by timely and accurately categorizing crime. By automating cyber law and providing timely legal answers to various reported cybercrimes, especially those concerning privacy and data protection, the model improves the capabilities of cybercrime units and achieves high accuracy and precision in anticipating pertinent legal sections.
Copyright (c) 2026 Sukrati Agrawal, Hare Ram Sah, Rajesh Kumar Nagar https://creativecommons.org/licenses/by-nc-nd/4.0
Ensemble Learning Approach for Efficient Recommendation Systems using Semi-Supervised Learning
In recommender systems, collaborative filtering (CF) is a crucial technique, but it often struggles with the data sparsity issue, which impact the recommendation accuracy. To address this challenge we have proposed a Co-training Ensemble Learning (CTEL) technique that integrates item-based Collaborative Filtering, user-based collaborative filtering (CF), and Singular Value Decomposition (SVD) through a structured stacking methodology to improve the recommendation performance. The co-training procedure, which creates pseudo-labels for unlabeled data based on a confidence threshold, is used to iteratively improve the user-based and item-based CF models after they have been originally trained. These models produce predictions for validation and test sets, in conjunction with the independently trained SVD model. These forecasts yield meta-features, which include extra statistical variables like variance and product of predictions. The Linear Regression model is trained as the meta-learner to find the prediction of the base models in the best possible way using K-Fold cross-validation. Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are used to assess the final model's performance on a test set. The outcomes confirm the effectiveness of the co-training and stacking strategy by showing notable increases in prediction accuracy when compared with existing techniques. Using the advantages of collaborative filtering approaches and matrix-based approach, the proposed model offers a comprehensive foundation for creating advanced recommendation systems.
Copyright (c) 2026 Nisha Sharma, Mala Dutta https://creativecommons.org/licenses/by-nc-nd/4.0
Evaluating Dijkstra and A* Pathfinding Algorithms for Mobile Robots in Warehouse Environments Using CoppeliaSim
In modern warehouse automation, mobile robots are essential for enhancing operational efficiency by autonomously navigating to pick and transport items. Effective path planning is crucial for these robots to move through complex environments, avoid obstacles, and minimize travel time. This study evaluates two prominent path planning algorithms Dijkstra and A*—implemented on a mobile robot within a simulated 3D warehouse environment using CoppeliaSim. Three distinct rack locations were analyzed to assess the performance of both algorithms concerning path optimality, computational efficiency, and real-time applicability. Simulation results indicate that while both algorithms successfully generated safe and accurate paths, A* outperformed Dijkstra in terms of speed and path efficiency. A*'s heuristic-driven approach resulted in lower computational load and faster execution time, making it more suitable for real-time warehouse operations where responsiveness is critical. The insights gained provide valuable guidance for robotics engineers and developers in selecting appropriate path planning strategies for autonomous navigation in industrial settings.
Copyright (c) 2026 Prabin Kumar Jha, Shambo Roy Chowdhury https://creativecommons.org/licenses/by-nc-nd/4.0
Towards Explainable Graph Spectral Clustering for BERT Embeddings
Artificial Intelligence algorithms are increasingly applied to tasks in Natural Language Processing, including document clustering. As these algorithms become increasingly complex (such as transformer-based embeddings, like BERT) and/or are of a ``black-box'' nature, such as Graph Spectral Clustering (GSC) algorithms, the demand for explaining the results of such algorithms is becoming increasingly urgent. In this paper, we propose a model-aware method to explain the results of GSC in the context of BERT-based embeddings. We present a novel theoretical methodology for explanation, based on the premise that document similarity in GSC is computed as cosine similarity of BERT embeddings of documents. We demonstrate the validity of this methodology by presenting strong GSC clustering results, restoring the human-made assignment of hashtags to tweets. We show that GSC based on BERT embeddings outperforms approaches using Term Vector Space and GloVe embeddings. Therefore, the resulting explanations are also expected to be of higher quality.
Copyright (c) 2026 Mieczysław Kłopotek, Sławomir T. Wierzchoń, Bartłomiej Starosta, Piotr Borkowski, Dariusz Czerski https://creativecommons.org/licenses/by-nc-nd/4.0
An Adaptive Hirarchical Meta Agent for Intelligent Congestion Control in IP Networks using Machine Intelligence
Modern IP networks face significant challenges in maintaining performance under dynamic and diverse traffic conditions. Traditional congestion control algorithms, such as TCP Reno, Cubic, and even recent reinforcement learning (RL) methods like PPO and DQN, often respond uniformly to packet loss, failing to distinguish between congestion-induced losses and those arising from wireless interference or hardware failures. This paper introduces AHMA (Adaptive Hierarchical Meta-Agent) — a novel, two-stage intelligent congestion control framework that integrates a Bayesian Transformer-based classifier with a Meta-Evolutionary Reinforcement Learning (Meta-ES-RL) controller. AHMA first classifies the cause of packet loss in real-time and then dynamically selects an optimized control strategy based on classification confidence. Using a synthetically generated NS-3 dataset of 1000 labeled flow samples, we evaluate AHMA’s performance against PPO, DQN, TCP Cubic, and TCP Reno across key metrics. Experimental results show that AHMA achieves a decision accuracy of 92%, reduces packet loss to 8.56% with improved throughput, and decreased latency, outperforming all baseline methods. This approach represents a significant advancement in adaptive, cause-aware congestion management, with strong potential for deployment in next-generation high-performance IP and 5G networks.
Copyright (c) 2026 Amit Kanungo, Prashant Panse https://creativecommons.org/licenses/by-nc-nd/4.0
Energy-Aware Cluster-Based Routing with Federated Learning Integration for Scalable IoT Environments
As a result of the Internet of Things' (IoT) explosive growth secure routing, energy optimization, and privacy preservation in resource-constrained environments have become major challenges. High overhead, static decision-making and susceptibility to malevolent attacks are common problems with traditional routing protocols. Federated Learning-Assisted Encrypted Routing based on Cost Function (FL-ERCF), an improved routing protocol that combines encrypted transmission with intelligent, privacy-preserving cluster head selection, is proposed in this paper to address these issues. Three main steps make up the proposed protocol: (i) link quality evaluation using metrics like RSSI, SNR, and variance; (ii) trust-based cluster formation directed by a federated learning model trained across dispersed IoT nodes to dynamically choose the best CHs; and (iii) secure data transmission using a lightweight symmetric encryption mechanism enhanced with digital certificates. Federated learning protects node-level privacy while improving flexibility and resilience to changing network conditions. The NetSim simulator is used to implement and assess the suggested FL-ERCF protocol, and it is compared to more established protocols like Energy Efficient Secure Routing (EESR) and Hybrid Secure Routing (HSR). Even in the face of an adversarial attack, experimental results show increased packet delivery ratios, decreased routing overhead, and enhanced throughput. A scalable and secure IoT routing framework that is appropriate for mission-critical applications, smart cities, and healthcare is established by this work.
Copyright (c) 2026 Ankur Sisodia, Swati Vishnoi, Shivshanker Singh, Nandini Sharma, Ajay Kumar Yadav https://creativecommons.org/licenses/by-nc-nd/4.0
Review of Hybrid Path Planning Techniques for Mobile Robots: Integration Between AI Techniques and Traditional Methods in Known Environments
Mobile robots require effective and secure path planning, especially in complex and dynamic environments. Traditional algorithms such as A*, Dijkstra, and Rapidly-exploring Random Trees (RRT) offer dependable and mathematical solutions but face challenges regarding scalability, adaptability, and processing requirements in real-time applications. On the other hand, artificial intelligence (AI) techniques, such as reinforcement learning (RL) and neural networks (NNs) provide flexibility and quick decision-making but face challenges such as data dependency, optimal solution, and computational overhead. This review analyzes hybrid path planning methodologies that integrate traditional algorithms with AI techniques, utilizing the advantages of both to overcome their limitations. Hybrid approaches improve scalability, collision avoidance, and re-planning efficiency by integrating the accuracy and reliability of traditional techniques with the adaptability and learning capabilities of AI. This review categorizes and analyzes research to identify significant gaps and suggests future paths for enhancing hybrid path planning, offering insights for the development of more robust and intelligent navigation systems for mobile robots and autonomous platforms.
Copyright (c) 2026 Mohamed Abdelghafar, Hazlina Selamat, Nurulaqilla Binti Khamis, Anas Aburaya, Mohd Taufiq Muslim https://creativecommons.org/licenses/by-nc-nd/4.0
Semantic-Aware Trajectory Planning for UAV in Dynamic Environments
Generating trajectories that leverage semantic information to guide a UAV safely and accurately to its destination in a dynamic environment remains an open problem. In the existing literature, semantics have been used to prioritize certain areas - either to guide the UAV through or to avoid them - for specific objectives, such as reducing errors in visual-inertial SLAM (VI-SLAM). However, prior work typically assumes a static environment when performing collision checking, even in cluttered and dynamic settings. We propose a two-stage workflow: The first stage performs semantic-aware pathfinding. The second stage optimizes the resulting path, incorporating kinematic constraints and performing collision checking that accounts for obstacle motion, while still ensuring real-time performance. To the best of our knowledge, this is the first approach that generates UAV trajectories by simultaneously leveraging semantic information and accounting for cluttered, dynamic environments. A summary video is available at https://youtu.be/I5w6AP7HThU
Copyright (c) 2026 Van Hung Nguyen, The Tien Nguyen, Tran Thang Le, Viet Hong Le https://creativecommons.org/licenses/by-nc-nd/4.0
Finding the Sweet Spot: A Study of Data Augmentation Intensity for Small-Scale Image Classification
Finding the optimal level of data augmentation intensity remains one of the most challenging aspects of training deep learning models on small-scale datasets. While data augmentation is universally recognized as essential for preventing overfitting and improving generalization, excessive augmentation can paradoxically harm model performance by introducing too much variability in the training data. This research investigates the "sweet spot" of augmentation intensity through a comprehensive study of six distinct augmentation strategies on CIFAR-10, a representative small-scale image classification benchmark. We designed a controlled experiment comparing: No Augmentation (baseline), Basic torchvision transforms, Light Advanced albumentations, Moderate Advanced geometric-photometric combinations, Strong Advanced with noise injection and dropout, and AutoAugment Style with complex transformations. Our findings reveal a clear quadratic relationship between augmentation intensity and model performance (R² = 0.85), with peak performance achieved at moderate intensity levels (0.21 on our normalized scale). The Basic augmentation strategy wins with 79.04% validation accuracy, significantly outperforming both minimal augmentation (77.15%) and excessive augmentation (72.17%). Through statistical analysis including effect size calculations, confidence intervals, and correlation studies, we demonstrate that the "sweet spot" lies in balanced augmentation that provides regularization benefits without overwhelming the learning process. These findings offer evidence-based guidelines for practitioners working with small-scale image datasets.
Use of Ternary Optimization in the Integrated Energy Systems
The rapid development of Integrated Energy Systems (IES), which unify diverse energy technologies such as electricity, heat, cooling, and gas, has heightened the importance of optimizing their operational modes. This paper explores the application of ternary optimization in IES, a discrete optimization approach where variables are constrained to three values: {-1, 0, +1}. Ternary optimization offers a balanced trade-off between binary and full-precision optimization, providing significant advantages in computational efficiency, memory savings, and energy efficiency. The article covers: key concepts of ternary optimization, including ternary representation, sparsity, and quantization; advantages and challenges of ternary optimization, such as reducing computational complexity and potential loss of accuracy; the application of ternary optimization for the IES. The role of ternary optimization in simplifying energy flow management, reducing computational resources, and enabling faster decision-making in dynamic environments is emphasized. Examples of using ternary optimization for energy distribution, microgrid management, integration of renewable energy sources, and energy storage systems are provided. A practical example of transforming an optimization model for IES into a ternary model using GMPL (GNU MathProg Language) is provided, demonstrating how ternary variables, constraints, and objective functions can be adapted. The paper concludes by discussing promising directions for ternary optimization in IES, including integration with AI and machine learning, development of specialized algorithms, and hardware support for ternary computations. Research underscores the potential of ternary optimization to enhance the efficiency, resilience, and scalability of IES, particularly in the context of increasing renewable energy integration and the complexity of modern energy grids.
Copyright (c) 2026 Vitalii Babak, Mykhailo Kulyk, Artur Zaporozhets, Svitlana Kovtun, Viktor Denysov https://creativecommons.org/licenses/by-nc-nd/4.0
ARIS: Autonomous Real-Time Interactive Social Robot
The purpose of this study is to develop and evaluate ARIS (A Real-time Interactive Social Robot), based on a Turtle 4 platform aimed at improving human-robot interactions (HRI) on university campuses. ARIS combines a 3D printed social robot structure with a software architecture based on 2D LiDAR, odometry, and IMU sensors for navigation and mapping, in addition to a voice assistant structured in 3 stages: audio recording, transcriptional processing, reaction, and reproductive synthesis. Experimental results show that the success rate of ARIS is greater than 86.5%, maintaining high accuracy in navigation and obstacle avoidance. The system also offers performance consisting of voice interaction with a total reaction latency of 1500 to 2200 ms. Access to low-cost robotic platforms allows students and researchers access to practical training, customization, and validation in the development of new technologies in social robotics.
Copyright (c) 2026 Cesar Minaya Andino, David Minango, Marcelo Zambrano https://creativecommons.org/licenses/by-nc-nd/4.0
Design of a Fuzzy PID Controller to Improve Electric Vehicles Performance Based on Regenerative Braking System
This study aims to improve energy storage in electric vehicle applications using a regenerative braking system. A brushless DC motor (BLDC) was chosen to power the vehicle due to its inherent advantages and suitability for electric vehicles. These vehicles require a low rate of error and stable and transient responses to make wheel acceleration smoother. The main objective of this study is to improve response time and stability under different operating conditions and overcome the drawbacks of traditional control techniques. Traditional PID controllers suffer from several problems, including transient overshoots, load fluctuations, and non-linear response, which lead to poor performance in electric vehicle propulsion systems. In this study, a fuzzy PID controller and a hysteresis current control loop are designed for the BLDC motor to address the above issues. The motor speed and battery charge state are verified using the MATLAB/Simulink environment in different situations to measure the reliability of the proposed controller. The results show that this proposed controller improves the dynamic performance of the regenerative braking system and enhances other operating characteristics such as maximum overshoot and settling time.
Implementation of Sand Cat Swarm Optimization for Uniform T-Way Test Suite Generation
T-way combinatorial testing is an essential approach for optimizing test suite generation by systematically covering parameter interactions while minimizing test cases. Various metaheuristic strategies have been introduced to improve test suite generation, with an increasing focus on balancing exploration and exploitation for efficient test selection. This study investigates the Sand Cat Swarm Optimization (SCSO) algorithm as a metaheuristic strategy for t-way test suite generation. Inspired by the hunting behavior of sand cats, SCSO dynamically adjusts sensitivity factors to improve test suite generation efficiency. To evaluate SCSO’s performance, 30 benchmark experiments were conducted across four groups of t-way configurations with t varying from 2 to 6 and v ranging from 2 to 10. Each configuration was executed five times, and the smallest test suite size was selected for analysis. Experimental results demonstrate that SCSO outperforms 15.79% of competing strategies, achieves comparable performance in 42.11% of cases, and is outperformed in 42.11% of benchmark comparisons. These findings highlight SCSO’s capability to generate competitive test suites, particularly in t-way interaction testing. The statistical evaluations, including Wilcoxon Rank and Friedman Mean Rank tests, further validate SCSO’s performance in comparison to other metaheuristic approaches. Although SCSO effectively reduces test suite size while maintaining interaction coverage, further enhancements are necessary to improve its adaptability and computational efficiency across diverse configurations. Future work should focus on refining SCSO’s exploration mechanisms to optimize search efficiency and extend its applicability in combinatorial test generation.
Copyright (c) 2026 Muhammad Aiman Mohd Asyraf, Mohd Zamri Bin Zahir Ahmad, Rozmie Razif Bin Othman, Ahmad Ashraf Abdul Halim, Kentaro Go, Nuraminah binti Ramli, R. Badlishah Ahmad, Latifah Munirah Kamarudin, Murad Muhammad Hasan Salih Al-Walidi https://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced UAV Path Planning Using the Tangent Intersection Guidance (TIG) Algorithm
Efficient and safe navigation of Unmanned Aerial Vehicles (UAVs) is critical for various applications, including combat support, package delivery and Search and Rescue Operations. This paper introduces the Tangent Intersection Guidance (TIG) algorithm, an advanced approach for UAV path planning in both static and dynamic environments. The algorithm uses the elliptic tangent intersection method to generate feasible paths. It generates two sub-paths for each threat, selects the optimal route based on a heuristic rule, and iteratively refines the path until the target is reached. Considering the UAV kinematic and dynamic constraints, a modified smoothing technique based on quadratic Bézier curves is adopted to generate a smooth and efficient route. Experimental results show that the TIG algorithm can generate the shortest path in less time, starting from 0.01 seconds, with fewer turning angles compared to A*, PRM, RRT*, Tangent Graph, and Static APPATT algorithms in static environments. Furthermore, in completely unknown and partially known environments, TIG demonstrates efficient real-time path planning capabilities for collision avoidance, outperforming APF and Dynamic APPATT algorithms.
Copyright (c) 2026 Hichem Cheriet, Badra Khellat Kihem, Samira Chouraqui https://creativecommons.org/licenses/by-nc-nd/4.0
A Simple Approach to Designing and Implementing a Fault Detection Device for Large Quantities of LED Boards
Light-emitting diodes (LEDs) play a crucial role in various practical applications. This paper presents a simple and cost-effective approach for detecting faults in LED boards with a large number. The types of faults detected include open circuits and short circuits. The proposed solution is based on a voltage threshold and a statistical algorithm to filter out noise. The paper first outlines the design concept, followed by the development and fabrication of a fault classification module. Subsequently, a control center is established to collect signals from all fault classification modules connected to LED boards in pairs. The received signals are processed and stored on a memory card. Finally, the resulting data can be accessed either from the memory card or directly via an external computer through a USB port. The experimental results demonstrate that the proposed design and fabricated device can accurately detect and classify faults on the tested boards. The data obtained can be used for statistical analysis and root cause investigation to enhance product quality.
Copyright (c) 2026 Van Anh Pham, Minh Tien Do https://creativecommons.org/licenses/by-nc-nd/4.0
Communication with the Manipulator using Gestures to Enhance the Manipulability of Persons with Reduced Mobility
The paper presents a robotic system that assists people with reduced mobility in the activities of picking up and putting down objects out of reach. Human‐robot communication is non‐verbal, using gestures that have been specifically selected for the robot’s use. Gestures are read out using an RGB‐D camera while the commands they express are executed online by a small UR3 cobot. The evaluation of the system has shown that it is useful and safe in the sense of the SUS and GQS respectively.
Copyright (c) 2026 Sebastian Koryl, Katarzyna Zadarnowska, Krzysztof Arent https://creativecommons.org/licenses/by-nc-nd/4.0
A Custom Robotic Vehicle Prototype for Disinfecting Workplaces
The SARS‐CoV‐2 pandemic has heightened the need for advanced and automated disinfection methods to ensure workplace safety and hygiene. This study presents the de‐ sign and implementation of a robotic vehicle capable of autonomously disinfecting high‐risk areas in diverse work environments based on human activity levels. The sys‐ tem integrates a machine vision module using YOLOv5 for real‐time human detection, a Decision Support System to assess contamination risks, an autonomous navigation module for path planning, and a user‐friendly interface for operator control. By leveraging real‐time data, the robot precisely applies disinfectant to identified high‐risk zones, dynamically adjusting the spray volume based on the level of contamination. The system was validated in a real‐world workplace setting, demonstrating its ability to autonomously perform targeted disinfection, offering a scalable solution to support workplace hygiene.
Integration of Trailer and Tractor Mobile Robot Considering Towing Dynamics
The industrial robots are used in different areas, one of these areas is towing trailer’s. This research proposes the use of an omniwheel mobile robot, designed specif‐ ically for towing purposes, to enhance the towing ca‐ pabilities. The study focuses on analyzing, controlling, and testing the dynamics of a trailer under the influence of external towing forces applied by an omniwheel mo‐ bile robot. The study includes dynamical analysis, path planning, tracking control, prototype design, tuning of selected actuators, and simulation results of the pro‐ posed omniwheel tractor robot. The dynamic equations, the path planning algorithm, the path tracking methods, and the PID tuning of actuators are tested in a MATLAB Simulink Simscape environment using the designed 3D models. The simulation results are used to fine‐tune the system parameters and minimize errors. Improved tuning of these parameters leads to better simulation outcomes. The final simulation results show that positional accuracy reaches 4.0 cm RMS precision (peak deviations: 7.6 cm) over a 6.63‐meter course, and speed deviations remain below %4.3. Angular control maintains stability by cap‐ ping errors at 0.117 rad (average: 0.008 rad) and limiting transient overshoot to %17.17 for aggressive maneuvers.
Copyright (c) 2026 Ünal Dana, Levent Çetin https://creativecommons.org/licenses/by-nc-nd/4.0
A Kinematic Prototype of a Compliant Artificial Prosthetic Knee Joint
This study presents the development and kinematic evaluation of a compliant artificial knee joint prototype fabricated using multi-material 3D printing. The design integrates a rolling-contact compliant mechanism—derived from the CORE and D-CORE concepts—into a prosthetic knee construction, aiming to replicate biological joint behavior while reducing weight, mechanical complexity, and friction. The prototype underwent iterative refinement, including geometric modifications, material selection for flexure bands, and structural asymmetry to mitigate overextension and increase durability. The resulting joint exhibited hybrid kinematic characteristics, blending features of both single-axis and polycentric knee mechanisms, and achieved a functional flexion range of approximately 142 degrees. Initial cyclic tests confirmed satisfactory stiffness and shape recovery of flexure bands with optimized dimensions, although long-term fatigue performance remains a challenge. A basic spring-damper system was also integrated, potentially aligning the prototype with K-1/K-2 prosthetic classification. However, full validation requires further mechanical testing in accordance with ISO 10328 standards, as well as optimization of the damping system for commercial viability. This research demonstrates the feasibility and potential of compliant mechanisms in lower-limb prosthetics while identifying critical areas for future development.
Innovation Capacities and Business Model in Colombian Farming Organizations
The main objective of the paper is to analyze the interaction between innovation capabilities and elements of the business model of farming organizations in Boyacá, Colombia. The study used a quantitative methodology, employing the Partial Least Squares Path Modeling (PLS-PM) technique. The theoretical model includes seven hypotheses that outline the relationships between innovation capabilities (sensing, seizing, and transformation) and business model (creation, delivery, and capture), The study revealed a significant correlation between the ability to detect and retain innovation and the development and implementation of business models. This underscores the significance of continuous learning and efficient alliance management in acquiring and utilizing environmental knowledge. The implications of this study are directed towards decision-makers, stakeholders, and policy makers within Colombia's National Agricultural Innovation System (NAIS). The study emphasizes the crucial role of a well-integrated system among its various actors in influencing the success of business models. It examines innovation as a dynamic capability and its interplay with vital components of the business model, thereby enhancing comprehension of business dynamics within this sector.
A Flexible Mobile Manipulator Architecture: A Case Study on Plasterboard Wall Preparation
Skilled labor shortage and a competitive market are challenges for many businesses. Hence, automation and robotization are well-established in the large-scale, well-structured industrial environments of many factories. Moreover, there is a push for safer and more easily programmable robots, so-called cobots, that would be more suitable for smaller-scale applications. However, the implementation and customization of an overall robot system with different sensors, tools, external systems, etc. for the use cases of in particular small and medium-sized enterprises (SMEs) are still often infeasible. Therefore, this paper describes the design, implementation, and testing of a flexible mobile manipulator architecture with the following three abstraction layers. A high-level semantic layer models and utilizes OWL ontologies of abstract manufacturing processes, specific workcell environments, linked context knowledge, and the current semantic world state. An intermediate translation layer collects and exchanges data from multiple system components via a unified internal database, which is used, e.g., in combination with behavior trees to convert symbolic, high-level actions into multiple numeric, low-level commands, including their parameter values. A low-level control layer is implemented locally on a mobile robot to provide a unified interface of its potentially customized subsystems to the higher layers. This architectural approach facilitates the implementation of new use cases, e.g., by a system integrator, in that, e.g., the ontologies and behavior trees can be adapted on a flexible model level without changing source code. The implemented system was adapted to and deployed in five different real-world use cases of industrial partners during the VOJEXT project. This paper focuses on the plasterboard wall preparation use case from the construction domain as a guiding example, which includes taping, spraying, and sanding operations.
Copyright (c) 2026 Łukasz Granat, Michał Bryła, Kuba Kamiński, Filip Jędrzejczyk, Sławomir Puchalski, Ingmar Kessler, Alexander Perzylo, Ángel Soriano https://creativecommons.org/licenses/by-nc-nd/4.0
Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach
A Single-Input Single-Output (SISO) linear system can represent various physical systems. One of the challenges in controlling practical systems is that the settling point and settling time cannot be prescribed together. In this paper, a novel Self-Tuning Control (STC) algorithm is presented, which ensures system stability while allowing the simultaneous determination of the settling time and settling point. Four possible scenarios will be defined to investigate the effectiveness of the proposed control method. Three different practices will be described in the simulation section for each scenario. Linear Quadratic Regulation (LQR) will be employed for comparison purposes. MATLAB software will be used to test and simulate these practices. The simulations will demonstrate that the STC is an optimal solution for SISO linear systems, comparable to LQR, with the significant advantage of guaranteeing the attainment of the settling point at the predefined settling time.
Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
This study compares two artificial intelligence approaches for parking occupancy detection: computer vision and convolutional neural networks (CNN). A dataset of 1,000 parking images was captured and labeled, using OpenCV in Python for computer vision processing and the YOLO V5 model for CNN. Results showed that the YOLO V5 model achieved 88% precision and 82% sensitivity, outperforming the computer vision method, which achieved 80% precision and 79% sensitivity. The research suggests that while CNNs offer superior performance, computer vision is a more economical option in contexts with limited resources. Future research will focus on evaluating the YOLOv7 version to reduce false positives and combining techniques to achieve a balance between accuracy and efficiency under variable conditions.
Grey Wolf Optimization Algorithm for a Concurrent Real-Time Optimization Problem in Game Theory
In this paper we present a grey wolf algorithm for a concurrent real time optimization problem in searching for optimal game solving solution. There are many solutions to solve the game. Each solution can demand different optimal values of different parameters. However some ways in which the players try to solve the game do not lead to success. The optimization problem consists of two phases. Each phase Impacts the second one in real time. The first phase is responsible for the optimization parameters choice. The second phase validates the choice and makes the optimization of the parameters. As a optimizing method we chose grey wolf optimization. At the beginning the algorithm generates some number of solutions. The solution which has the value of the parameters the closest to maximum is a position of an alfa wolf. The rest of solutions are, according to the values of the parameters, split to be the positions of beta, delta and omega wolfs.
Design, Implementation, and Performance Optimization of a ROS Based Autonomous Mobile Robot for Intralogistics in Manufacturing Facilities
This article presents the development and optimization of a ROS-based Autonomous Mobile Robot (AMR) designed for intralogistics in manufacturing environments. By integrating robust mechanical design, advanced sensor fusion, and the Hybrid A* path-planning algorithm, the AMR offers significant improvements in flexibility, safety, and efficiency over traditional AGVs. The proposed AMR system enables dynamic, autonomous navigation in complex industrial layouts, responding adaptively to obstacles and varying payloads. Validation in simulated and real-world settings demonstrates the system’s performance in navigation accuracy, energy efficiency, and load-handling capability, highlighting its potential to streamline logistics and reduce operational costs in Industry 4.0 settings.
Copyright (c) 2026 Neslihan Demir, Pinar Demircioglu, Ismail Bogrekci https://creativecommons.org/licenses/by-nc-nd/4.0
Research on a Robust Adaptive Controller with Disturbance Observer for Wheeled Mobile Robot
This paper presents a robust adaptive controller for wheeled mobile robots (WMRs) designed to effectively compensate for disturbances and system uncertainties. The proposed control scheme includes an inner loop with a PID controller for wheels speed control and an outer loop with a disturbance observer to track the trajectory and minimize position errors. The outer loop control signal is derived based on the WMR's dynamic and kinematic models, while the disturbance observer adapts to uncertainties and external disturbances. The system's stability is proven in sense of Lyapunov theory. The performance of the proposed controller is validated through simulations, which show significant improvements in trajectory tracking compared to existing method. Furthermore, experimental results confirm that the controller maintains stability and robustness under varying load conditions and different trajectory paths, demonstrating its effectiveness in real-world applications
Copyright (c) 2026 Trong Tai Nguyen, Doan Phuc An Nguyen, Dai Nghia Tran, Phuc Bao Nguyen Nguyen, Thanh Dat Mai https://creativecommons.org/licenses/by-nc-nd/4.0
High-Performance Electric Two-Wheeler Fast Charger Based on Intelligent Control Algorithm
Electrification in the transportation sector, such as electric two-wheelers (E2W), including electric bicycles and motorcycles, must be accompanied by the development of a sufficient ecosystem. Meanwhile, currently, available commercial E2W battery chargers cannot keep up with user mobility in terms of charging time, which implicates the inflexibility of E2W in daily usage and needs to meet safety standards according to the battery specification. This work proposed a charging system to meet the need for fast and safe charging in conventional E2W batteries. It used an interleaved buck converter (IBC) regulated by a combination of proportional integral–fuzzy logic control (PI-FLC) algorithms to ensure safe and fast charging. This system is adaptable and potentially meets the universal principle by allowing users to adjust current and voltage based on the power source and vehicle specifications. The PI-FLC algorithm determines the optimal charging current by considering charging voltage, current, and state of charge (SoC). The IBC’s ripple cancellation feature enhances compactness by reducing the output filter capacity. The system uses nickel manganese cobalt (NMC) batteries with a specification of 72 V at 20 Ah with a 1 C charging rate. The performance was compared to PID CC-CV and PI-CV algorithms. The results showed that the proposed system produces a suitable voltage and current charging characteristic and only needs 57.75 minutes to charge 0-100% SoC, while PID CC-CV takes 180 minutes and PI CV takes 240 minutes.
Copyright (c) 2026 Subiyanto Subiyanto, Rizky Ajie Aprilianto, Mario Norman Syah, Bagaskoro Saputro, Abdurrakhman Hamid Al-Azhari, Nektar Cahayasabda, Bayu Adi Pambudi, Faiq Mananul Faqih, Icha Arifah Annisa, Dwi Bagas Nugroho, Siva Khaaifina Rachmat, Dewi Anggriani https://creativecommons.org/licenses/by-nc-nd/4.0
Hybrid Cascaded ANFIS-RBFNN Based Controller for PV-Driven Grid System
Solar photovoltaic energy is gaining popularity in modern distribution networks due to its clean energy attributes. In order to maximize PV power generation conversion, the application of Maximum Power Point Tracking is essential. Henceforth, this work presents a novel hybrid MPPT approaches based on Cascaded Adaptive Neuro Fuzzy Inference System and Radial Basis Function Neural Network to achieve rapid and greatest PV power extraction while ensuring zero oscillation tracking with a Single-Ended Primary Inductor Converter. The SEPIC converter efficiently regulates the output voltage to match grid requirements while maintaining high power conversion efficiency. The Cascaded ANFIS and RBFNN are combined to enhance MPPT accuracy and robustness under varying environmental conditions. The cascaded architecture enables seamless transition between the two controllers, ensuring optimal performance across an extensive range of operating conditions. The MATLAB/Simulink is used for analyzing the efficacy of the proposed system and the proposed converter and MPPT approach is compared with existing topologies for proving the importance of the developed work. The outcomes demonstrate that the proposed SEPIC converter achieves reduced THD of 1.16% and the Cascaded ANFIS-RBFNN based MPPT approach attains higher tracking efficiency of 99.61% with rapid convergence speed and execution time compared to traditional techniques. Overall, this paper represent a promising direction towards achieving more efficient and sustainable PV-driven grid integration.
Copyright (c) 2026 Blessy A. Rahiman, J. Jayakumar, Rajasekaran Meenal https://creativecommons.org/licenses/by-nc-nd/4.0