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Automation, Volume 6, Issue 1 (March 2025) – 7 articles

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8 pages, 4590 KiB  
Article
Design, Fabrication, and Characterization of a Novel Crawling Pneumatic Soft Robot
by Huaqing Wu, Yutong Han, Xinyu Chen, Rong Lu, Erxing Zhuang, Huaping Wu, Xiaodi Jiang, Xiaojun Tan and Bo Cao
Automation 2025, 6(1), 7; https://doi.org/10.3390/automation6010007 - 12 Feb 2025
Viewed by 21
Abstract
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 [...] Read more.
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
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20 pages, 2792 KiB  
Article
Method for Testing Combinational Circuits by Multiple Diagnostic Features Using Weight-Based Sum Codes Properties
by Dmitry V. Efanov, Dmitry V. Pivovarov, Nicolás Cortegoso Vissio, Alexander O. Kuptsov and Daniil E. Egorov
Automation 2025, 6(1), 6; https://doi.org/10.3390/automation6010006 - 26 Jan 2025
Viewed by 453
Abstract
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 [...] Read more.
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
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29 pages, 1007 KiB  
Article
Advanced Data Classification Framework for Enhancing Cyber Security in Autonomous Vehicles
by Shiva Ram Neupane and Weiqing Sun
Automation 2025, 6(1), 5; https://doi.org/10.3390/automation6010005 - 25 Jan 2025
Viewed by 965
Abstract
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, [...] Read more.
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
(This article belongs to the Special Issue Next-Generation Cybersecurity Solutions for Cyber-Physical Systems)
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36 pages, 10540 KiB  
Article
Design and Modeling of an Intelligent Robotic Gripper Using a Cam Mechanism with Position and Force Control Using an Adaptive Neuro-Fuzzy Computing Technique
by Imad A. Kheioon, Raheem Al-Sabur and Abdel-Nasser Sharkawy
Automation 2025, 6(1), 4; https://doi.org/10.3390/automation6010004 - 18 Jan 2025
Viewed by 567
Abstract
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 [...] Read more.
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
(This article belongs to the Collection Smart Robotics for Automation)
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18 pages, 6414 KiB  
Article
Stimuli-Induced Equilibrium Point-Based Algorithm for Motion Planning of a Heavy-Load Servo System
by Ziping Wan, Nanbin Zhao and Guang’an Ren
Automation 2025, 6(1), 3; https://doi.org/10.3390/automation6010003 - 7 Jan 2025
Viewed by 543
Abstract
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 [...] Read more.
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
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21 pages, 3710 KiB  
Article
Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control
by Igor Gulshin and Olga Kuzina
Automation 2025, 6(1), 2; https://doi.org/10.3390/automation6010002 - 27 Dec 2024
Viewed by 765
Abstract
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 [...] Read more.
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
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23 pages, 4048 KiB  
Article
Universal and Automated Approaches for Optimising the Processing Order of Geometries in a CAM Tool for Redundant Galvanometer Scanner-Based Systems
by Daniel Kurth, Colin Reiff, Yujiao Jiang and Alexander Verl
Automation 2025, 6(1), 1; https://doi.org/10.3390/automation6010001 - 25 Dec 2024
Viewed by 514
Abstract
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 [...] Read more.
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
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