International Journal of Mechanical Engineering and Robotics Research
IJMERR is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in mechanical engineering and robotics.
Artificial Intelligence in Controlling Mechanical Vibrations: An Active Control Systems Perspective
Mechanical vibrations have a big effect on how reliable, accurate, and long-lasting mechanical, robotic, and mechatronic devices. Conventional strategies to control vibrations work well in certain situations, but they are frequently unable to keep working when there are nonlinearities, unknowns, and changing dynamics over time. Artificially intelligent-based control techniques emerged as effective remedies to these issues, facilitating adaptive, information-driven and capable of learning vibration prevention. An extensive simulation-based study of artificial intelligence methods for actively mechanically control of vibration is demonstrated in this article. Neural network training is incorporated into intelligent vibration control frameworks using a focused on control systems paradigm. Lyapunov concept is used to give a mathematical evaluation of stability for machine learning control systems. When compared to traditional PID and LQR control algorithms, computer simulations on an elastic mechanical framework show enhanced vibration reduction. Prospective study guidance, implementation obstacles, and practical implications are highlighted. The findings demonstrate that artificial intelligence-based vibration control is a reliable and expandable solution for the robotics and mechanical devices.
Copyright (c) 2026 International Journal of Mechanical Engineering and Robotics Research
A Numerical Analysis of Different Micro-Textures in Improving Tribological Properties of Surfaces
Laser surface texturing has become an important technology to improve the tribological properties of surfaces under lubricated friction. While several studies have analyzed how different micro-textures affect hydrodynamic pressure generation, further study of how different characteristics of the micro-texture geometry affect pressure generation remains needed. This study aims at conducting a comparative analysis of an optimized and modified chevron micro-texture with other conventional micro-textures used in literature using computational fluid dynamic (CFD) simulations. The study showed that the modified chevron micro-texture with a large leg-width ratio outperformed other conventional micro-texture shapes such as the circle and rectangle by generating an asymmetric pressure distribution. Ongoing research is being done to experimentally verify the results, which can be significant for various applications.
Topic: Tribology: Friction, Wear, and Lubrication
Multi-objective Optimization of the 7075 Aluminum Alloy Milling Process: A Comparative Study of MOPSO, NSGA-II, SPEA2, and MOACO
This study investigates the effect of machining parameters and optimization of surface roughness (Ra) and material removal rate (MRR) in milling 7075 aluminum alloy based on a comparative analysis of the MOPSO, NSGA-II, SPEA2, and MOACO algorithms. Three machining parameters, including spindle speed (S), feed rate (f), and depth of cut (d), were considered. The results of the analysis of variance (ANOVA) showed that S and f had significant effects on Ra (p < 0.05), contributing 20.06% and 79.68%, respectively, while f and d were significant for MRR (p < 0.05), accounting for 45.01% and 40.11% of contribution, respectively. The predictive model for Ra using the Group Method of Data Handling (GMDH) indicates that the Double (2 variables) function exhibits exceptional predictive performance, with an R² value of 0.9990 on the training set and 0.9962 on the validation set. Multi-objective optimization results indicate that NSGA-II provides the most stable solutions, while SPEA2 and MOACO show less effectiveness in providing optimally balanced solutions based on the observed distribution. The MOPSO algorithm has a fast convergence speed; however, the optimal values are highly dispersed and unstable for both objectives. The performance of these optimization algorithms was validated by experiments. The results show that the mean deviation of Ra between predicted and actual values is 6.5%, while that of MRR is 0.37%. These findings indicate that all four algorithms in this study have certain effectiveness in multi-objective optimization of the trade-off between Ra and MRR in machining.
Development and Balancing Control of a Unicycle Robot Using Omnidirectional Wheel
Mobile robots are being studied by many researchers in fields such as automotive, logistics, and robotics. Moreover, they are used in areas like transportation, healthcare, and personal assistance because they can move flexibly, fast, and accurately. Unicycle robots are one of the mobile robots that have unique characteristics, including designing, constructing, moving, and controlling. The unicycle robot uses one wheel for moving forward and backward and needs one reaction wheel to make itself stay upright while it is moving. In this paper, researchers design new mechanisms for the unicycle robot wheel to be appropriate for them to move in any direction that combines the pitch wheel and roller wheel into one wheel; the wheel is called an omnidirectional wheel. The control system uses an STM32 as the main controller that operates to work with the driver motor, IMU sensor, and encoder sensor. The LQR controller is applied to control the unicycle robot. Experimental results compare simulation and actual experiment to show time, angle, speed, and torque.
Development of a Single-Seater Electric Vehicle for Electrathon America Competition
A Electraton Americas is an organization that promotes the development of electric vehicles for competition. The present work is focused on the design of the America single-seater structure which was based mainly on the protection of the driver, space for various accessories, improve range and aerodynamics. The design was carried out in strict adherence to the Electrathon America 2021 manual. The 3D CAD modeling was done in SolidWorks software. Aluminum and steel profiles with diameters of ½ in and ¾ in with a thickness of 2.7 mm were used. The regulations specify that tests must be performed on the structure of the single-seater, mainly torsion and bending tests to ensure its correct operation with a minimum safety factor of 2; the structural analyses were carried out in Ansys software. Additionally, a fluid analysis was carried out. In addition, with strain gauges, measurements of the structural deformations were made to validate the stress analyses performed. A functional single-seater vehicle was obtained, with a safe structure, aerodynamic, suspension and adequate steering.
Application of the DfT Methodology for the Design and Validation of Exoskeletons Used in Hand Rehabilitation for Patients with Cerebral Vascular Accident
This study investigates the application of the Design for Testability (DfT) methodology to define mechanical and functional evaluation criteria in the design of a hand exoskeleton for use in the rehabilitation of cerebral vascular accidents (CVA). To this end, recent studies presenting methodologies for evaluating mechanical and/or functional aspects in the development of hand exoskeletons for use in CVA rehabilitation were first reviewed. Based on this and taking into account DfX concepts, a DfT methodology for CVA hand exoskeletons was developed and verified with a case study for patients with a 50th percentile. The prototype was designed in 3D modeling software, manufactured, and verified using the proposed methodology. The results show that the application of the DfT methodology allowed for the comprehensive development and evaluation of hand exoskeletons for CVA, covering everything from the design stage to manufacturing, resulting in better mechanical and functional characteristics and low cost in line with the needs of patients CVA.
Topic: Fracture Mechanics and Fatigue Analysis
Recent Advances in Welding Processes for High-Entropy Alloys: A Comprehensive Review
High-entropy alloys (HEAs) have gained attention for their exceptional mechanical properties, corrosion resistance, and unique microstructure. However, welding HEAs remains challenging, attributed to intricate microstructures, inadequate thermal conductivity, and elevated melting points. This review systematically summarizes recent advances in metal welding of HEAs with both similar and dissimilar alloys, including stainless steel, titanium alloys, aluminum alloys, and nickel-based alloys. Welding techniques, including gas metal arc welding (GMAW), laser welding (LW), electron beam welding (EBW), and gas tungsten arc welding (GTAW) are critically compared in terms of joint microstructure, mechanical performance, and corrosion behavior. The influence of filler composition, heat treatment, and process parameters on weld quality are discussed. Key challenges, including microstructure evolution under complex service conditions and controlling brittle intermetallic phases, are identified. Finally, potential research directions and application prospects of HEA welding in advanced manufacturing are outlined.
Diagnosis of Multiple Bearing Faults in BLDC Motors Using ReliefF Feature Selection and Random Forest Classifier
Bearing faults remain a leading cause of failure in Brushless DC (BLDC) motors, making early and accurate diagnosis critical for industrial reliability. This study proposes an intelligent multi-class fault diagnosis system that integrates ReliefF-based feature selection with a Random Forest classifier. Vibration data were collected from seven bearing conditions using a BLDC motor testbed. From each signal, 18 statistical features were extracted across time and frequency domains. The ReliefF algorithm was employed to identify the most discriminative features, significantly reducing dimensionality while preserving diagnostic relevance. The Random Forest classifier was then trained on the selected features to distinguish between normal and six fault classes, including compound fault types. Experimental results show that the model achieved perfect classification accuracy on the training set and 92.2% on the testing set. Confusion matrices and scatter plots validate the model’s effectiveness in fault classification, with strong generalization and minimal misclassification. This framework offers a robust, efficient solution for real-world bearing fault diagnosis in BLDC motors.
Topic: Mechanical Fault Diagnosis Based on Transfer Learning
Development and Measurement of the Jumping with Pneumatic Cylinder
This study presents the design and testing of a lightweight jumping robot powered by a pneumatic cylinder and elastic elements. Experiments varying the number of rubber bands revealed that four bands provided the best balance of rebound force and air consumption, achieving a maximum vertical jump height of 270 mm and a rebound cycle of 0.8 s. The results confirm that pneumatic–elastic actuation enables efficient, repeatable jumps without complex control. Beyond competitions, the robot’s use of low-cost, readily available materials such as rubber bands and air tanks highlights its potential for rapid assembly and deployment in disaster zones, where conventional equipment is limited. The proposed approach offers a practical framework for developing agile, resource-efficient robots capable of overcoming obstacles in emergency conditions.
The Nonlinear Study of Composite Plates Natural Frequency Using RNN-LSTM combined with Wavelet Transform Features
In this research, we discuss how we extract features related to natural frequency of nonlinear composite plates (NCP) using a combination of Wavelet Transform (WT) and artificial intelligence (AI) algorithms. The nonlinearity is represented in the plate geometry and boundary conditions (BCs). Our findings, which build on previous works by the authors, indicate that WT can effectively reflect the natural frequency features of NCP. However, we also noted that this approach can be quite complex, involving numerous calculations and iterations, which means it might not be ideal for quickly and accurately extracting natural frequency. To tackle this issue, we’ve developed a new AI model designed to learn from and test these results by extracting key natural frequency features that reveal crucial information about the NCP behavior. The AI model is based on a recurrent neural network with long short-term memory blocks (RNN-LSTM) since the natural frequency datasets have a time-dependent and memory-dependent behavior. Our results strongly suggest that the proposed technique is a promising approach, especially for complex structures in varying environmental conditions.
A New Adaptive Neural Fuzzy Sliding Control Method for Dynamic Nonlinear Plants
This paper proposes a New Adaptive Neural Fuzzy Sliding Mode Controller (NANFSMC) for the regulation of a Coupled Tank System (CTS), targeting the adaptive control of unknown dynamic nonlinear systems subject to external disturbances, sensor noise, and other uncertainties in experimental environments. The CTS exhibits strong nonlinearities and uncertainties arising from sensor noise, parameter variations, variations in output valve characteristics, and significant time delays. The proposed control architecture integrates two synergistic components. The first is an adaptive control component that employs a Radial Basis Function Neural Network (RBFNN) to approximate the adaptive control law, with an adaptive updating mechanism in compensating for RBFNN approximation errors. The second is a sliding mode control component whose parameters are updated in real time via a fuzzy inference mechanism to enhance robustness. Both control laws are derived within the framework of Lyapunov stability theory, ensuring closed-loop stability under all operating conditions. The proposed approach is experimentally validated and benchmark in comparison with an adaptive neural sliding mode ANSMC and an optimal PID controller. Experimental results demonstrate that the proposed NANFSMC achieves superior tracking performance, robust stability, and adaptability to diverse uncertainties, including sensor noise, external disturbances, and time-varying reference signals.
Particle Swarm Optimization-Based Hazardous Configuration Identification and Dynamic Analysis for Industrial Robots
This paper takes the KUKA KR3 R540 six-degree-of-freedom (6-DOF) industrial robot as the research object. Based on the kinematic model established using the standard D-H method, the Newton-Euler dynamic theory is introduced to construct the multi-body dynamic model of the robot. To identify the dangerous working postures that cause the maximum joint torque, a search strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed, and Adams-MATLAB co-simulation is adopted for solution. With the maximization of the sum of absolute values of each joint driving torque as the optimization objective and joint angles as decision variables; MATLAB drives the PSO algorithm to generate candidate postures and calls Adams for high-precision dynamic solution, automatically traversing the high-dimensional joint space and iteratively searching for the global optimal solution. Simulation results show that this method can effectively locate the global most hazardous configuration and obtain the corresponding maximum total torque for each joint, providing crucial load boundary conditions for the robot's structural strength verification and lightweight design, while also offering references for the selection of key components for structural optimization.
Topic: Autonomous Robots in Industry 4.0
Pose Estimation-Driven Control of Humanoid Upper Arms for Human Motion Mimicry
This paper presents a real-time teleoperation framework that maps human upper-body motion, captured by a single RGB-D camera, to a dual-arm upper humanoid robot designed with low-cost servos. The system employs MediaPipe-based pose estimation and a torso-anchored coordinate transformation to achieve operator-centric retargeting that is robust to variations in camera placement and subject geometry. To suppress tremor and sensor noise, a constant-velocity Kalman filter combined with an adaptive dead-zone is applied to wrist trajectories, ensuring smooth motion while maintaining responsiveness. A min–max scaling function with saturation enforces safe workspace mapping, while joint commands are computed using damped-least-squares inverse kinematics with joint-limit and self-collision checks. The execution layer incorporates incremental speed-aware stepping to emulate continuous trajectories on servo actuators. Experimental results demonstrate accurate static pose reproduction, robust dynamic path following, and zero joint-limit violations, achieving an average wrist-tracking RMSE of 12.4 mm and median end-to-end latency of 86 ms. The platform is reproducible, cost-effective, and adaptable for applications in education, rehabilitation, and human–robot collaboration.
Dynamics-Driven Policy Transfer to Heavy Wheel-Legged Robots via Imitation and Optimization
Policy transfer is an efficient approach for developing specific robots. Its effectiveness depends on high-quality imitation datasets and a stable learning process. However, substantial differences in geometry and dynamics between source and target robots pose challenges. Purely kinematics-driven mapping methods and manual parameter tuning often fail to maintain kinematic-dynamic consistency. In this study, we transfer control policies from the quadruped robot Unitree Go1 to our self-developed heavy wheel-legged robot Tiangou. We propose a Consistency-Aware Retargeting (CAR) method. This extends conventional inverse kinematics by adding dynamic consistency constraints. Using motion data from Go1’s MPC controller, CAR generates a reference dataset for Tiangou. We then integrate Bayesian Optimization (BO) into the imitation learning framework. This enables autonomous tuning of policy model structures and optimization hyperparameters. Experiments show that CAR reduces foot-end position errors, mitigates joint angular velocity fluctuations, and decreases foot-end slippage. Moreover, Bayesian optimization improves sample efficiency and training stability. These contributions establish a practical foundation for policy transfer across heterogeneous robotic platforms.
Topic: Recent Advances in Mobile Robotics Navigation
Prediction of Wear Behavior in Surface Contact using CDM and ANN models
The objective of this study is to investigate and predict wear behavior in contacting surfaces through an integrated approach that combines Continuum Damage Mechanics (CDM) and Artificial Neural Networks (ANN). Pin on disk wear experiments were performed under dry conditions using three engineering materials ST37, C45E4, and Al 7075 with variations in load, sliding speed, and hardness systematically designed using a full factorial Design of Experiments (DOE). The CDM model quantified material degradation and estimated wear coefficients, which were then used as training data for a feed-forward back-propagation ANN. Both models were validated against independent experimental data. Results indicate that the ANN model achieved high prediction accuracy (average error <5%), outperforming the CDM model (average error ≤12%). Scanning Electron Microscopy (SEM) revealed adhesive wear as dominant in the steels, while Al 7075 exhibited reduced wear due to higher hardness. The interaction effects showed that load and sliding speed have significant influences on wear, whereas hardness plays a secondary role. The findings establish a robust framework for wear prediction, process optimization, and potential real-time monitoring in engineering applications, demonstrating the effective integration of physics based and data-driven modeling in predictive tribology.
A Hybrid Bayesian ICA-LSTM Framework for Unsupervised-Like Anomaly Detection in Rolling Element Bearings
This study proposes a hybrid anomaly detection framework based on Independent Component Analysis (ICA) and Bayesian Long Short-Term Memory (LSTM) networks for early fault diagnosis in rolling element bearings. The model is trained solely on normal condition data from the CWRU Bearing Dataset, enabling unsupervised-like deployment suitable for real-world industrial environments. Vibration signals are preprocessed using ICA to extract statistically independent features, followed by a Bayesian LSTM that captures temporal dynamics and provides uncertainty-aware predictions. A dynamic thresholding mechanism based on Interquartile Range (IQR) is implemented to autonomously distinguish between normal and anomalous behavior without manual calibration. Experimental results demonstrate exceptional performance with 99.1% accuracy, perfect recall (100%) on fault conditions, and zero false negatives, ensuring no fault is missed. The composite anomaly score effectively tracks degradation progression, making the system highly reliable for predictive maintenance applications. This approach combines statistical signal processing with probabilistic deep learning to deliver a robust, interpretable, and adaptive solution for bearing health monitoring.
Topic: Recent Innovations in Mechanical Vibrations
The Effect of Preheating on The Quality of Spot Welding Between Dissimilar Mild Steel and Stainless Steel 304
Welding dissimilar materials often presents specific challenges due to differences in thermal properties, expansion, and chemical reactions between the joined metals. This study assesses the effects of preheating mild steel and AISI 304 stainless steel plates on tensile strength, elongation, and material hardness. The variations in preheat temperature used are 100°C, 120°C, 150°C, and 180°C. Welding mild steel and stainless steel AISI 304 while preheating shows that 180°C is the best preheating temperature for getting an average tensile strength of 12.41 kgf/mm² and an average strain of -0.655. The hardness test results show that a preheat of 120°C provides optimal hardness in the weld area (106.1 HR), HAZ (96.7 HR), and base metal (103.9 HR). More than that, spot welding between mild steel that has not been treated in any way and stainless steel AISI 304 that has been heated up shows that a temperature of 180°C for the heating up process gives the best improvement in plasticity, with a tensile strength of 15.94 kgf/mm² and an average elongation of 1.7970. Hardness test data show that preheating at 180°C provides the highest hardness in the weld area (109.30 HR), and 120°C provides the highest hardness in the HAZ (99.03 HR). In contrast, the base metal decreases hardness with increasing preheat temperature, with the lowest value at 180°C (77.63 HR). The selection of the correct preheat temperature is crucial to ensure optimal weld joint quality.
Modal Analysis-Informed Topology Optimization for Dynamic Performance Enhancement of Horizontal Canned Motor Pumps
This study proposes an integrated framework for ship applications, combining high-fidelity finite element model simulation, experimental modal analysis, and topology-based structural optimization to enhance the dynamic performance of the horizontal shielded pump under actual installation constraints. A high-fidelity finite element model was developed that accurately considers material properties and boundary conditions. This model was validated through impact hammer tests, with the maximum modal frequency deviation found to be less than 2%. Modal analysis revealed that the shielded pump exhibits a "low-frequency global-high-frequency local" vibration mode, with key deformation regions concentrated at the base transition section, flange, and outlet section. Based on topology optimization and modal energy distribution, the research team implemented a targeted "reinforcement-lightweight-augmentation" strategy, which included strengthening the base support, thinning the non-load-bearing hull, and reinforcing the outlet section. After optimization, the first-order natural frequency of the shielded pump was significantly increased from 42.31 Hz to 76.86 Hz, a 81.7% improvement. The average enhancement of higher-order modes was approximately 18%, with the vibration modes becoming more uniform, effectively suppressing local vibrations. The method proposed in this study not only significantly improves the modal stability of the shielded pump and reduces resonance risk but also provides valuable practical guidance for vibration control and structural lightweight design of high-speed rotating machinery in the shipbuilding industry.
System Classification of Hand Movements in Hand Prosthesis Prototype Based on Model Machine Learning Control
The constraint of loss of limb function will cause difficulties in carrying out normal activities. Moreover, limbs, especially hands, play an important role in most human activities. Various studies on the development of automatic control of hand prostheses have been carried out. The use of appropriate sensors and the availability of certain working algorithms in the design and construction of hand prostheses are very important to improve the functionality of the hand prosthesis control system aspect. Thus, the machine learning model control system, on the one hand, the prosthesis prototype is designed to be able to move the hand automatically based on the classification algorithm carried out by machine learning. The FSR402 sensor on the force sensor reading system can be used as input for the Master Controller. In the Master Controller, there is a deep learning neural network system that uses the ANN (Artificial Neural Network) 32 neuron architecture model with an accuracy value of 99.05% and a loss value of 0.0284. The model deployed to the Master Controller produces a prediction value above 90% for all labels. The designed hand prosthesis control system has successfully moved the hand prosthesis based on ten predetermined labels.
Path Optimization using an Improved APF-RRT* Algorithm
Path planning remains a critical research area in mobile robotics, yet current approaches often suffer from suboptimal path quality, limited sampling efficiency, and inadequate adaptability across diverse operational scenarios. To address these issues, this paper proposes an improved algorithm combining Artificial Potential Field (APF) and Restricted Path Time (RRT*) approaches. This algorithm employs an optimization model that combines dynamic sampling with potential field guidance, constructing a two-stage dynamic sampling mechanism. During sampling, candidate nodes with Gaussian noise are generated along the resultant force direction. Finally, path cost comparison and parent node reselection are performed within the dynamic optimization radius to ensure asymptotic optimality of the path. Experimental results show that in complex maps, path length is reduced by 33.41% and 26.64%, respectively, and planning time is reduced by 21.36% and 86.32%, respectively; in narrow passages, path length is reduced by 49.6% and 49.8%, respectively. The results confirm the effectiveness of the two-stage dynamic sampling mechanism, which not only preserves the probabilistic completeness of the RRT* algorithm but also adaptively adjusts the sampling strategy, improving both planning length and time.
Modified Connective Assembly Model for Error Propagation Analysis of High-Precision Assembly of Mechanical Components
High-precision mechanical assemblies require accurate error propagation models to precisely predict error accumulation in the final assembly and to ensure optimal performance and reliability. Conventional linear models exhibit limited accuracy in predicting geometric errors. To address these limitations, this study proposes a modified connective assembly model based on second-order nonlinear error propagation using homogeneous transformation matrices. The proposed model is implemented in Python, and its effectiveness is validated against existing linear and fully nonlinear connective assembly models to evaluate its accuracy and computational efficiency. At an angular orientation error of 1°, the linear model exhibits a fixed error of 0.40 mm in the Z-direction, failing to capture the error of 0.10 mm observed in the fully nonlinear model. The developed model reduces this discrepancy to 0.23 mm, achieving a 56.67% improvement in accuracy over the linear model. Similarly, under 1 mm geometric run-out tolerance, the linear model exhibits an error of -1.60 mm in the Z-direction, while the fully nonlinear model predicts -1.70 mm error. The modified model narrows this gap to -1.66 mm and delivers a 60% improvement. Moreover, the execution time comparisons based on Monte Carlo simulation (MCS) show that the proposed model is computationally efficient, achieving a 48.39% reduction in execution time compared to the fully nonlinear model.
Compliant Mechanisms: Implementation of topological optimization method for the development of robotic gripper with flexible finger
This study presents the design, optimization, and experimental validation of low-cost, 3D-printed robotic grippers utilizing compliant mechanisms. The development is based on a three-dimensional Topology Optimization Method (TOM) implemented with the Solid Isotropic Material with Penalization (SIMP) approach. The Method of Moving Asymptotes (MMA) was employed to solve the associated nonlinear optimization problems for synthesizing flexible fingers, while a quadratic approximation method was utilized for designing rigid support structures. Two distinct objective functions were formulated: the first aimed to maximize the output displacement of a monolithic polylactic acid (PLA) finger by exploiting material compliance, and the second focused on minimizing the weight of the support structures under stiffness constraints. The optimized designs were fabricated via additive manufacturing and assembled into two-, three-, and four-finger gripper configurations. Experimental evaluation on an ABB IRB-120 robotic arm demonstrated the grippers' exceptional adaptability in manipulating objects of varied geometries and surface textures. Furthermore, payload capacity tests revealed maximum loads of 1.5 kg, 2.2 kg, and 2.7 kg for the two-, three-, and four-finger grippers, respectively. The results confirm that the proposed methodology, centered on MMA-based 3D topology optimization, provides an effective framework for developing high-performance, low-cost compliant robotic grippers.
Developing A Vision-Guided Tracked Robot for Fire Emergency Missions
The rapid development recently in urbanization and industrial processes in Vietnam has significantly transformed living and production spaces, leading to a notable increase of fire and explosion incidents in both the frequency and complexity. Fire incidents can lead to devastating consequences to human lives, environment and national resources. Firefighting is a life-threatening profession, exposing firefighters to extreme heat, toxic smoke, collapsing structures, and explosive hazards. These risks make rescue and fire suppression operations highly dangerous and complex. As urbanization and industrialization intensify fire incidents, there is an urgent need to deploy intelligent firefighting robots. Equipped with sensors, cameras, and autonomous navigation, these robots can operate in hazardous environments, reduce human exposure, and enhance the efficiency of emergency response. To address the life-threatening risks associated with firefighting operations, this study presents the design and control of a tracked autonomous firefighting robot capable of navigating complex terrains and detecting fire hazards through a vision-guided approach. The robotic system integrates a machine vision module, centered on the YOLOv8 deep learning model for real-time fire detection. A depth camera is employed to capture environmental data, from which a deviation angle and distance to the fire source are computed and transmitted to a PLC-based control unit via Modbus RS485 communication protocol. For autonomous navigation and obstacle avoidance, the robot utilizes a Bug 2 algorithm, informed by LiDAR sensor input. Experimental evaluations demonstrate the robot’s ability to accurately detect fire sources and efficiently maneuver around obstacles, validating its potential as a reliable solution for hazardous firefighting scenarios.
Dynamic Modeling and Control of Pneumatic Artificial Muscles-Driven Multi-Fingered Robots for Circular Object Manipulation
Recently, great concerns have been brought up when applying biologically-inspired robots in rehabilitation for individuals with movement disability. This type of robots must have a higher level of safety feature- provided by more flexible construction. Pneumatic Artificial Muscle (PAM) is driven by compressed air and its performance is similar to biological muscles. Then PAMs are great candidates of actuators for rehabilitation robots. This paper investigates a control algorithm for a multi-fingered robot actuated by PAMs in grasping and manipulating a circular-shaped object. By using the Lagrange method in combination with the natural force-length-velocity relationship of the contracting muscle, a dynamic model for the general robot-object system was formulated. Based on this dynamic model, control algorithms for stable grasping and dexterous manipulation of the object by the multi-fingered robot were proposed. The asymptotic convergence of the closed-loop dynamics was analyzed using the Lyapunov principle and the extended LaSalle invariance theorem. Simulation results further validated the effectiveness of the proposed control algorithms.
Development of Flexible Clamping Devices for Precision Workholding of Irregular and Thin-Walled Workpieces
This paper presents the design, simulation, and experimental validation of a flexible clamping fixture specifically developed for machining irregular and thin-walled workpieces components that are traditionally difficult to fixture due to their non-uniform contours and low structural stiffness. The proposed system features modular segmented jaws capable of conforming to a wide range of geometries without damaging the part or requiring custom fixture fabrication. Through detailed CAD design and finite element analysis, the fixture's structural integrity, clamping force distribution, and deformation behavior are rigorously evaluated. Experimental results confirm the system’s adaptability and effectiveness in securely holding asymmetric and delicate parts under real machining conditions. The developed fixture not only reduces setup time but also enhances positioning accuracy, repeatability, and overall productivity, offering a practical and versatile solution for modern precision manufacturing.
Copyright (c) 2025 International Journal of Mechanical Engineering and Robotics Research
Optimization Strategies for Electric Propulsion in UAVs: A Review of Technologies, Control Systems, and Environmental Impacts
This paper presents a systematic literature review of recent developments in the optimization of electric propulsion systems for Unmanned Aerial Vehicles (UAVs). The study focuses on technological advancements, control system enhancements, and environmental impacts to provide a holistic perspective on current trends and future research directions. A total of 42 peer-reviewed articles, published between 2014 and 2024, were analyzed using the PRISMA method to ensure transparency and rigor in selection. The findings show a significant shift toward intelligent optimization techniques, especially Particle Swarm Optimization (PSO) and its hybrid variants, which are widely used for tuning Fuzzy PID controllers to enhance system performance and stability. Furthermore, innovations in lightweight composite materials and the integration of Internet of Things (IoT) technologies have demonstrated improved endurance and adaptability of UAV propulsion systems. Despite these advancements, most studies remain at the simulation stage, with limited real-world implementation. Environmental modeling, thermal-aware control, and lifecycle analysis are identified as key areas requiring further investigation. This review highlights the necessity of cross-disciplinary approaches to address the complexity of electric propulsion optimization. Recommendations for future work include the adoption of embedded ML models, experimental validation frameworks, and system-level integration with aerodynamic and mission planning subsystems.
Design, Programming, and Evaluation of an Economic Portable Muscle Activity Measurement Device
Current commercial EMG systems struggle with four main drawbacks: limited mobility, high prices, time-sensitive processing demands, and their function suitability for a wide range of clinical and research environments. This study will solve these issues through the development of an affordable compact EMG system that ensures real-time processing stability. The system design unites surface electrodes with a low-noise amplifier and a microcontroller having performance-enhanced processing ability. The design requirements specify a signal spectrum from 0 to 250 Hz and precise resolution because this combination leads to the correct measurement of muscular activities. The device performed tests through a series of experiments with healthy participants to measure signal accuracy, noise levels, and usability conditions in controlled and semi-controlled testing environments. The results showed a mean signal-to-noise ratio of 28.5 dB with accuracy levels equivalent to commercial systems, which proved that the device provides clean EMG signals across diverse conditions. Results of noise power analysis showed 0.020 µV² in the resting state but decreased to 0.012 µV² when muscles reached their peak activation strength since effective noise reduction measures were implemented. Despite its advantages including cost-effective operation along with portability, the system presents restrictions in usage with clinical patients who need additional validation of its effectiveness. The portable EMG device presents a versatile, low-cost apparatus for rehabilitation services, sports science testing, and healthcare monitoring needs and requires continuing development for practical implementation.
Special Issue: Intelligent Mechatronics Systems: Advances in Autonomous Control and Robotics (Closed)
Expert-Rule-Based Gain Adjustment for PID and Fuzzy Controllers under Step and Continuous Inputs
Robotic manipulators' ability to perform precise and repeated operations makes them indispensable in industrial settings. Even with advances in control strategies, achieving ideal performance over several input profiles remains a challenging problem. Four control systems are evaluated in this work: fuzzy logic control, fuzzy expert system for step input control, fuzzy expert system for smooth function control, and the fuzzy-based expert-rule gain adjustment method. Using smooth input profiles across joints R, T, and Z, the method examines the system's response to step setpoints of 10, 3.75, and 5, as well as larger step setpoints of 30, 11.25, and 15. The performance criteria assessed include overshoot, rise time, settling time, integral absolute error, and root mean square error. Under all testing conditions, the results show that the suggested approach consistently outperforms the others. Its low overshoot and root mean square error for step setpoints ensure accurate and consistent results. Larger step setpoints demonstrate excellent performance with balanced metrics, emphasizing scalability and robustness. Especially in joints T and Z, it shows notable adaptability to smooth input profiles and achieves low integral absolute error and root mean square error. These results highlight how effectively the system combines the advantages of step and smooth input control. This work shows that under different input conditions, the proposed control method guarantees accuracy, stability, and adaptability, offering a reliable and flexible solution for dynamic robotic systems. For practical applications, it marks a significant improvement in robotic manipulator performance.
Assoc. Prof. Dr. Phichitphon Chotikunnan
Characterization and Validation of R410A Condensation Performance in Split Air Conditioning Units: Experimental Investigation and ANSYS Fluent Simulation
This study introduces a novel approach to optimizing the condensation performance of R410A refrigerant in split air conditioning systems, offering actionable insights for industry and addressing critical challenges of energy efficiency and climate adaptation in regions such as the Kurdistan Region of Iraq. By integrating advanced experimental instrumentation with high-fidelity Computational Fluid Dynamics (CFD) modeling, it presents a comprehensive investigation of two-phase flow dynamics across the full length of an 8.914 m copper condenser tube. Forty-one precisely calibrated temperature sensors and pressure transducers provide real-time monitoring of thermal and flow behavior, while CFD simulations employ the Volume of Fluid (VOF) method with geometric reconstruction and level set interface tracking to accurately predict phase distributions and vapor void fraction. Results reveal three distinct condensation zones: a desuperheating region (0-1.8 m) where superheated vapor rapidly cools, a condensation zone (1.8-6 m) characterized by near-saturation temperatures and a sharp decline in vapor fraction, and a subcooling zone (6-8.9 m) dominated by liquid-phase sensible heat removal. The correlation between experimental and CFD results demonstrates robust model validation, with an overall prediction error of 20-22%. The study highlights the critical role of stratified two-phase flow in maximizing heat transfer efficiency and confirms that the condenser design achieves complete condensation and subcooling, ensuring only liquid refrigerant exits the system. These findings are directly relevant to manufacturers seeking to improve the performance and reliability of split air conditioning units under demanding climatic conditions, both locally and in similar environments worldwide.
Modeling of the Anti-Slip System Processes in the Wheel-Rail Contact of a New Generation Electric Locomotive
This article is devoted to the creation of a simulation model of anti-slip system processes in the "wheel-rail" contact. For this purpose, the necessary elements and model blocks were created using the specialized software environment Dymola: 1) the Sub-Wagon model of a single wagon using Modelica Mechanics Translational; 2) the traction control and speed regulation system model for the new generation KZ8A locomotive, which is compatible with other Modelica blocks and can be used instead of the ideal wheel without slip; 3) a wheel-rail contact model designed for the analysis of wheelset slip during traction and sliding during braking; 4) a computer model of the anti-slip system. Another feature of this model is the consideration of the geographical characteristics of the real Agadyr-Darya section when simulating the movement of rolling stock, which consists of a single section of the new generation KZ8A electric locomotive and 35 freight wagons. An algorithm was developed using Harel's state machine formalism, implemented in Modelica StateMachine, and experimental adhesion characteristics were obtained for various friction conditions of the rail. The developed model allows for the synthesis of an adaptive speed regulator that takes into account local wheel-rail adhesion characteristics as well as track gradients. The results of this work are aimed at improving the efficiency of rolling stock, reducing fuel/energy consumption, and minimizing wheel and rail wear, which together will result in significant economic benefits.