Automation

A suspended cable-driven parallel robot is a type of lightweight large-span parallel robot. The stability and control of this multi-input multi-output robot are studied in this work to overcome its inherited vulnerability to disturbance. An adaptive fuzzy synergetic controller is proposed to overcome these issues, combining synergetic control theory with adaptive fuzzy logic to ensure robust trajectory tracking. The parameters of the controller are optimized using the Dragonfly Algorithm, a metaheuristic technique known for its simplicity and fast convergence. The adaptive fuzzy synergetic controller is tested on a suspended cable-driven parallel robot model under both disturbance-free and disturbed conditions, demonstrating global asymptotic stability and superior tracking accuracy compared to existing controllers. Simulation results show the proposed controller achieves minimal tracking error and improved robustness in the presence of dynamic uncertainties, validating its practical applicability in industrial scenarios. The findings highlight the effectiveness of integrating synergetic control, fuzzy logic adaptation, and optimization for enhancing the performance and reliability of suspended cable-driven parallel robots. Full article

Rotating machines predominantly operate under healthy conditions, leading to a limited availability of fault data and a significant class imbalance in diagnostic datasets. These challenges hinder the development and deployment of fault diagnosis methods based on deep learning in practice. Considering these issues, a novel hierarchical adaptive wavelet-guided adversarial network with physics-informed regularization (HAWAN-PIR) is proposed. First, a hierarchical wavelet-based imbalance severity score is used to quantify the data imbalance within the datasets. Second, HAWAN-PIR generates synthetic fault data in the time domain via multiscale wavelet decomposition and represents the first attempt to embed physics-informed regularization to incorporate relevant fault knowledge. The quality of the synthetic fault data is then evaluated via a comprehensive multiscale synthesis quality index. Furthermore, a scale-aware dynamic mixing algorithm is proposed to optimally integrate synthetic data with real data. Finally, a one-dimensional convolutional neural network (1-D CNN) is employed for extracting features and classifying faults. The effectiveness of the proposed method is validated through two case studies: motor bearings and planetary gearboxes. The results show that HAWAN-PIR can synthesize high-quality fake data and improve the diagnostic accuracy of the 1-D CNN by 17% for the bearing case and 15% for the gearbox case. Full article

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of the Artificial Protozoa Optimizer (APO) with the powerful optimization strategies of Differential Evolution (DE) to address complex and engineering design challenges. The HPDE algorithm is designed to balance exploration and exploitation features, utilizing innovative features such as autotrophic and heterotrophic foraging behaviors, dormancy, and reproduction processes alongside the DE strategy. The performance of HPDE was evaluated on the CEC2014 benchmark functions, and it was compared against two sets of state-of-the-art optimizers comprising 23 different algorithms. The results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out of 30 from the first set and 23 functions from the second set. Additionally, HPDE has been successfully applied to a range of complex engineering design problems, including robot gripper optimization, welded beam design optimization, pressure vessel design optimization, spring design optimization, speed reducer design optimization, cantilever beam design optimization, and three-bar truss design optimization. The results consistently showcase HPDE’s good performance in solving these engineering problems when compared with the competing algorithms. Full article

Autonomous navigation is essential for mobile robots to efficiently operate in complex environments. This study investigates Q-learning and Deep Q-learning to improve navigation performance. The research examines their effectiveness in complex maze configurations, focusing on how the epsilon-greedy strategy influences the agent’s ability to reach its goal in minimal time using Q-learning. A distinctive aspect of this work is the adaptive tuning of hyperparameters, where alpha and gamma values are dynamically adjusted throughout training. This eliminates the need for manually fixed parameters and enables the learning algorithm to automatically determine optimal values, ensuring adaptability to diverse environments rather than being constrained to specific cases. By integrating neural networks, Deep Q-learning enhances decision-making in complex navigation tasks. Simulations carried out in MATLAB environments validate the proposed approach, illustrating its effectiveness in resource-constrained systems while preserving robust and efficient decision-making. Experimental results demonstrate that adaptive hyperparameter tuning significantly improves learning efficiency, leading to faster convergence and reduced navigation time. Additionally, Deep Q-learning exhibits superior performance in complex environments, showcasing enhanced decision-making capabilities in high-dimensional state spaces. These findings highlight the advantages of reinforcement learning-based navigation and emphasize how adaptive exploration strategies and dynamic parameter adjustments enhance performance across diverse scenarios. Full article

The present paper proposes a new Maximum Torque Per Ampere (MTPA) algorithm applied to sensorless speed control for the Synchronous Reluctance Machine (SynRM). The SynRM mathematical model is suitably modified and expressed in the γδ estimated reference frame, which could be applied in sensorless implementations. In the controller–observer scheme, an MTPA controller is coupled with a sliding mode observer (SMO) of first order. The provided equivalent control inputs are directly utilized by a modified EMF observer to estimate the rotor speed and position. Also, the MTPA control, SMO, and modified EMF observer are accordingly expressed in the γδ reference frame. In the duration of the SynRM operation, the developed MTPA algorithm succeeds in adjusting both stator current components in the γ-axis and δ-axis to the maximum torque point, while the SMO converges rapidly, achieving the coincidence between the γδ and dq reference frames. In addition, a simple torque decoupling technique is used to determine the γ-axis and δ-axis reference currents connected with the Anti-Windup Controller (AWC) for stator current control. Despite conventional MTPA methods, the proposed MTPA control strategy is designed to be robust in a wide speed range, exhibiting a high dynamic performance, regardless of the presence of external torque disturbances, reference speed variation, and even current measurement noise. The performance of the overall observer–control system is examined and evaluated using MATLAB/Simulink and considering noisy current feedback. Simulation results demonstrate the robustness and effectiveness of the proposed MTPA-based control method. Full article

To be successful in a circular economy, it is important to keep the cost of operationalizing remanufacturing processes low in order to retain as much value of the product as possible. Optimizing operations for disassembly, as a key process step, is therefore an important prerequisite for economically viable circular manufacturing. The generation of fit-to-resource disassembly instructions is labor-intensive and challenging because (digital) product information is often lacking at End-of-Life. With upcoming EU regulations for Eco-design for Sustainable Products in mind, including the future use of Digital Product Passports, it is time to think about standardized methods to capture disassembly information for products. First requirements from small and medium-sized remanufacturing companies have been collected and compared with available frameworks for modeling product topology, parameters, and (dis)assembly process rationale. Based on this, the disassembly hypergraph is presented as a concept for recording ‘resource-agnostic disassembly guides’ in (machine-readable) product models to determine required disassembly actions and tools ‘smartly’. The concept builds upon existing models. Additionally, suitable methods for the collection of disassembly information are explored, resulting in preliminary insights from disassembly data collection workshops. Although the approach is promising, future work is needed to expand the concept of the disassembly hypergraph with both guidelines for setting up disassembly ontologies and further systematic disassembly knowledge extraction in order to apply this as a useful means for companies to rationalize their disassembly operations. Full article

Integrating futuristic humanoids like Tesla Optimus into supply chain operations represents groundbreaking automation and workforce efficiency innovation. This study investigates the potential of humanoids to address critical supply chain challenges, such as labor shortages, rising operational costs, and the demand for sustainable practices. Considering its ability to handle worker-intensive, hazardous, and repetitive duties, humanoids could offer an alternative to business challenges like inefficient operations, health and safety concerns, and worker shortages. Intelligent robotics plays an essential role in improving productivity, supporting sustainability, and transforming workforce dynamics as supply chains become increasingly complex. The study examines the effects of humanoids on workforce reallocation, manufacturing sustainability, and supply chain productivity. The current research reviews the usefulness, advantages, and downsides of integrating humanoids into supply chains. This study uses a mixed-method approach, incorporating case studies, qualitative productivity data, and expert interviews. According to Tesla, Optimus could significantly enhance supply chain efficiency by reducing error rates, streamlining workflows, and enabling 24/7 operations. It could also help meet sustainability goals by lowering waste and energy consumption. The study limits Tesla’s experience, modern technologies, and inadequate information from various industrial and geographical contexts. However, this study will be eye-opening for industries requiring such humanoid robots for their operations. Additional studies need to deal with factors like high implementation expenses, potential job displacement, and flexibility in changing supply chain demands. While focused on Tesla, this study provides insights that can inform broader applications of humanoid robotics in supply chains across industries. This study presents an in-depth review of humanoid involvement in developing future supply chain models. It also offers helpful knowledge that will assist industries in considering adopting comparable robotic integration as a strategic decision. Full article

This paper presents a method for position correction in collaborative robots, applied to a case study in an industrial environment. The case study is aligned with the GreenAuto project and aims to optimize industrial processes through the integration of various hardware elements. The case study focuses on tightening a specific number of nuts onto bolts located on a partition plate, referred to as “Cloison”, which is mounted on commercial vans produced by Stellantis, to secure the plate. The main challenge lies in deviations that may occur in the plate during its assembly process, leading to uncertainties in its fastening to the vehicles. To address this and optimize the process, a collaborative robot was integrated with a 3D vision system and a screwdriving system. By using the 3D vision system, it is possible to determine the bolts’ positions and adjust them within the robot’s frame of reference, enabling the screwdriving system to tighten the nuts accurately. Thus, the proposed method aims to integrate these different systems to tighten the nuts effectively, regardless of the deviations that may arise in the plate during assembly. Full article

Soft robots have shown great application potential in human–computer interaction, scientific exploration, and biomedical fields. However, they generally face issues like poor load capacity. Inspired by the propagation and movement mechanisms of ocean waves, this study proposes a novel type of pneumatically driven crawling soft robot. An automated pneumatic drive system was first constructed for driving and controlling the crawling soft robot, and then the soft robot body was made using additive manufacturing and silicone molding. Experimental testing of the robot’s performance revealed that it can move efficiently on surfaces with varying friction coefficients and has a strong load-bearing capacity. This work is expected to provide a reference for the design of other soft robots. Full article

The paper puts forth a methodology for the arrangement of calculation control at the outputs of combinational digital devices based on utilizing multiple diagnostic features. The first diagnostic feature is the belonging of the formed codewords to a pre-selected weight-based sum code. The second and third diagnostic features are the control of calculations by the belonging of calculated functions describing data and checking bits of sum codes to the class of self-dual and related functions. The arrangement of calculation control by multiple diagnostic features is founded upon the implementation of Boolean signal correction during the synthesis of the embedded control circuit. This is conducted in consideration of the established characteristics of weight-based sum codes, wherein the functions describing their checking bits exhibit identical values on pairs of data vectors inversed in all bits (these are the so-called self-quasidual functions). The utilization of Boolean signal correction on the orthogonal to all input variable combinations enables the additional determination of correction function values. This ensures the self-duality of functions describing data bits and, consequently, the self-quasiduality of functions describing checking bits. The paper proposes a structure for arranging the calculation control in accordance with the three specified diagnostic features. Furthermore, it develops the algorithm of correction function additional determination that underlies the synthesis of the embedded control circuit. It should be noted that the algorithm does not require the analysis of the values of functions calculated at the outputs of the object of diagnostics; instead, it automatically allows for the performance of additional determinations. This facilitates its utilization when integrated with computer-aided logic design systems. The paper presents a case study of the process of obtaining correction functions and simulates the operation of the synthesized self-checking device, thereby demonstrating the effectiveness of the proposed method. Full article

Autonomous vehicles (AVs) have revolutionized the automotive industry by leveraging data to perceive and interact with their environment effectively. Data safety is essential for supporting AV decision-making and ensuring reliability in complex environments. AVs continuously collect data from multiple sources like LiDAR, RADAR, cameras, and ultrasonic sensors to monitor road conditions, traffic signals, and pedestrian movements. An effective data classification framework is crucial for managing vast amounts of information and securing AV systems against cyber threats. This paper proposes a comprehensive framework for AV data classification, categorizing data by sensitivity, usage, and source. By integrating a review of the literature, real-world cases, and practical insights, this study introduces a novel data classification model and explores sensitivity criteria. The findings aim to assist industry stakeholders in creating secure, efficient, and sustainable AV ecosystems. Full article

Manufacturers increasingly turn to robotic gripper designs to improve the efficiency of gripping and moving objects and provide greater flexibility to these objects. Neuro-fuzzy techniques are the most widespread in developing gripper designs. In this study, the traditional gripper design is modified by adding a suitable cam that makes it compatible with the basic design, and an adaptive neuro-fuzzy inference system (ANFIS) is used in a MATLAB Simulink environment. The developed gripper investigates the follower path concerning the cam surface curve, and the gripper position is controlled using the developed ANFIS-PID. Three methods are examined in the developed ANFIS-PID controller: grid partitioning (genfis1), subtractive clustering (genfis2), and fuzzy C-means clustering (genfis3). The results show that the added cam can improve the gripping strength and that the ANFIS-PID model effectively handles the rise time and supported settling time. The developed ANFIS-PID controller demonstrates more efficient performance than Fuzzy-PID and traditional tuned-PID controllers. This proposed controller does not achieve any overshoot, and the rise time is improved by approximately 50–51%, and the steady-state error is improved by 75–95%, compared with Fuzzy-PID and tuned PID controllers. Moreover, the developed ANFIS-PID controller provides more stability for a wide range of set point displacements—0.05 cm, 0.5 cm, and 1.5 cm—during the testing period. The developed ANFIS-PID controller is not affected by disturbance, making it well suited for robotic gripper designs. Grip force control is also investigated using the proposed ANFIS-PID controller and compared with the Fuzzy-PID in three scenarios. The result from this force control proves objects’ higher actual gripping performance by using the proposed ANFIS-PID. Full article

To tackle the problems of power saturation and high energy consumption of the heavy-load servo system in a servo process, we propose a motion planning algorithm based on the stimuli-induced equilibrium point (SIEP), named the SIEP-MP algorithm. First, we explore the correlation between various modes of the bionic eye system and the heavy-load servo system through head-eye motion control theory and derive the core formula of the SIEP-MP algorithm from psychological field theory. Then, we design a speed loop of the heavy-load servo system by combining a speed controller and a disturbance observer. Furthermore, we create a position loop of the heavy-load servo system by combining a position controller and a feed-forward controller. We verify the low-pass filtering and range-limiting functions of the SIEP-MP algorithm by building the experimental platform, designing the target trajectory, and setting the control parameters. Experimental results demonstrate similar command filtering, elimination of power saturation, and energy-saving functions compared to low-pass filters, and the algorithm has a better mode-switching performance. The proposed SIEP-MP algorithm can ensure the optimal tracking performance of the heavy-load servo system in different modes through mode switching. Full article

This study presents an automated control system for wastewater treatment, developed using machine learning (ML) models integrated into a Supervisory Control and Data Acquisition (SCADA) framework. The experimental setup focused on a laboratory-scale Aerobic Granular Sludge (AGS) reactor, which utilized synthetic wastewater to model real-world conditions. The machine learning models, specifically N-BEATS and Temporal Fusion Transformers (TFTs), were trained to predict Biological Oxygen Demand (BOD₅) values using historical data and real-time influent contaminant concentrations obtained from online sensors. This predictive approach proved essential due to the absence of direct online BOD₅ measurements and an inconsistent relationship between BOD₅ and Chemical Oxygen Demand (COD), with a correlation of approximately 0.4. Evaluation results showed that the N-BEATS model demonstrated the highest accuracy, achieving a Mean Absolute Error (MAE) of 0.988 and an R² of 0.901. The integration of the N-BEATS model into the SCADA system enabled precise, real-time adjustments to reactor parameters, including sludge dose and aeration intensity, leading to significant improvements in granulation stability. The system effectively reduced the standard deviation of organic load fluctuations by 2.6 times, from 0.024 to 0.006, thereby stabilizing the granulation process within the AGS reactor. Residual analysis suggested a minor bias, likely due to the limited number of features in the model, indicating potential improvements through additional data inputs. This research demonstrates the value of machine learning-driven predictive control for wastewater treatment, offering a resilient solution for dynamic environments. By facilitating proactive management, this approach supports the scalability of wastewater treatment technologies while enhancing treatment efficiency and operational sustainability. Full article

The combination of highly dynamic systems with a limited work envelope with a less dynamic system with a larger working envelope promises to combine the advantages of both systems while eliminating the disadvantages. For these systems, separation algorithms determine the trajectories based on the target geometries. However, arbitrary processing orders of these result in inefficient trajectories because successive geometries may be geometrically far apart. This causes the dynamic system to operate below its potential. Current planning tools do not optimise the processing order for such redundant systems. The aim is to design and implement a planning tool for the application of laser marking. The tool considers the processing order of the 2D geometries from a geometric point of view. The resulting sequenced path data can then be used by trajectory generation algorithms to make full use of the potential of redundant systems. The approach analyses literature on Travelling Salesman Problems (TSP), which is then transferred to the given application. A heuristic and a genetic algorithm are developed and integrated into a planning tool. The results show the heuristic algorithm being faster while still producing solutions whose total path length is similar to that of the genetic algorithm. Even though the solutions don’t meet any optimality standards, the presented automated approaches are superior to manual approaches and are to be seen as a starting point for further research. Full article

A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given the constraints of battery, bandwidth, multi-hop, mobility, and enormous network sizes. Recently, Swarm Intelligence (SI) methods have been employed in MANET routing due to similarities between swarm behavior and routing. These methods are applied to obtain ideal solutions that ensure flexibility. This paper implements an enhanced Particle Swarm Optimization (EPSO) algorithm that improves MANET performance by enhancing the routing protocol. The proposed algorithm selects the stable path by considering multiple metrics such as short distance, delay of the path, and energy consumption. The simulation results illustrate that the EPSO outperforms other existing approaches regarding throughput, PDR, and number of valid paths. Full article

Electric Vehicles (EVs) are set to play a crucial role in the energy transition. Although EVs offer significant environmental benefits, their technology still faces major challenges related to performance optimization, energy efficiency improvement, and cost reduction. A key point to address these challenges is the accurate identification of the speed/torque operating points of the drive systems. However, this identification is generally achieved using mechanical sensors, which are fragile, bulky, and expensive. This paper aims to develop, implement, and validate a speed/torque observer in real time based on the Extended Kalman Filter (EKF) approach for an EV equipped with an Open-End Winding Induction Motor with Dual Inverter (OEWIM-DI). The implementation of the EKF is based on the state modeling of the OEWIM-DI, enabling the observation of the torque and speed using voltage and current measurements. The validation of this approach is conducted experimentally on the FPGA and DS1104 boards. The results show that this approach offers excellent performance in terms of accuracy, stability, and real-time response speed. These results suggest that the proposed method could significantly contribute to the advancement of EV technology by providing a more robust and cost-effective alternative to traditional mechanical sensors while improving the overall efficiency and performance of EV drive systems. Full article

A method for cooperative transportation, which required formation change in a traveling environment, is gaining interest. Deep reinforcement learning is used in formation changes for multi-robot cases. The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. On the other hand, re-learning may be required in unrecognized circumstances by using the MADDPG method. Although the development of MADDPG using model-based learning and imitation learning has been applied to reduce learning time, it is unclear how the learning results are transferred when the number of robots changes. For example, in the GASIL-MADDPG (Generative adversarial self-imitation learning and Multi-agent Deep Deterministic Policy Gradient) method, how the results of three robot training can be transferred to the four robots’ neural networks is uncertain. Nowadays, Scaled Dot Product Attention (SDPA) has attracted attention and is highly impactful for its speed and accuracy in natural language processing. When transfer learning is combined with fast computation, the efficiency of edge-level re-learning is improved. This paper proposes a formation change algorithm that allows easy and fast multi-robot knowledge transfer using SDPA combined with MAPPO (Multi-Agent Proximal Policy Optimization), compared to other methods. This algorithm applies SDPA to multi-robot formation learning and performs fast learning by transferring the acquired knowledge of formation changes to a certain number of robots. The proposed algorithm is verified by simulating the robot formation change and was able to achieve dramatic high-speed learning capabilities. The proposed SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) learned 20.83 times faster than the Deep Dyna-Q method. Furthermore, using transfer learning from a three-robot to five-robot case, the method shows that the learning time can be reduced by about 56.57 percent. A scenario of three-robot to five-robot is chosen based on the number of robots often used in cooperative robots. Full article

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings. Full article