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PID-fuzzy switching-based strategy to heading control for remote operated vehicle

Neural Computing and Applications 2024年 1-17页

作者： Xie, Baolong He, Shuping Cao, Xiang Wang, Honghai Stojanovic, Vladimir Shi, Kaibo School of Electrical Engineering and Automation Anhui University Hefei230039 China School of Artificial Intelligence Anhui University Hefei230039 China School of Computing and Artificial Intelligence Chaohu University Chaohu238024 China Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil Engineering University of Kragujevac Kraljevo36000 Serbia School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China

Accurate heading control is the premise for underwater vehicles to complete underwater operations. In order to improve the performance of heading control, this paper proposes a heading control strategy based on PID-fuzzy switching. First, the force analysis of the steering motion was carried out to derive the dynamics model of the steering motion. Then, the whole heading control process is divided into two phases, steering phase and holding phase. Fuzzy control is used in the steering phase, and PID control is used in the holding phase. By setting the automatic switching switch, the control system can switch freely between the two to meet the needs of system adjustment under different errors. Finally, the performance of the established heading control system is verified through motion control simulations and pool experiments, and the proposed control strategy is compared with other methods. The results show that the control strategy can effectively achieve ROV heading control with good steady-state accuracy, and the overall performance has been greatly improved. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy control

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Energy Minimization using Custom-Designed Magnetic-Spring Actuators

Energy Minimization using Custom-Designed Magnetic-Spring Ac...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yue Yang Fu Ali U. Kilic David J. Braun Department of Electrical and Computer Engineering SyBBURE Searle Undergraduate Research Program Vanderbilt University Advanced Robotics and Control Laboratory Advanced Robotics and Control Laboratory Vanderbilt University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study introduces an innovative actuator that resembles a motor with a non-uniform permanent magnetic field. We have developed a prototype of the actuator by combining a standard motor, characterized by a uniform magnetic field, with a custom rotary magnetic spring exhibiting a non-uniform magnetic field. We have also presented a systematic computational approach to customize the magnetic field to minimize the energy consumption of the actuator when used for a user-defined oscillatory task. Experiments demonstrate that this optimized actuator significantly lowers energy consumption in a typical oscillatory task, such as pick-and-place or oscillatory limb motion during locomotion, compared to conventional motors. Our findings imply that incorporating task-optimized non-uniform permanent magnetic fields into conventional motors and direct-drive actuators could enhance the energy efficiency of robotic systems.

关键词： Actuators Energy consumption Limbs Systematics Prototypes Motors Minimization Magnetic fields Springs Standards

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Job Shop Scheduling Using Genetic and Heuristic Exchange Algorithms for AGVs

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Journal of Institute of control, robotics and Systems 2022年第2期28卷 191-201页

作者： Wang, Jun-Kyung Eoh, Gyuho Park, Tae-Hyoung Department of Control Robot Engineering Chungbuk National University Korea Republic of Industrial AI Research Center Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

Solving a job shop scheduling problem (JSSP) entails allocating entire jobs to machines to minimize the processing time. Generally, the transit time between machines is not considered in the conventional JSSP because it is shorter than the processing time. In a real production environment, however, the transit time of an autonomous ground vehicle (AGV) is long due to the need to solve random problems, such as finding an indirect route or avoiding collisions. Moreover, AGVs sometimes need to transport the job on directional roads with upstream and downstream constraints. Therefore, we present an AGV job scheduling algorithm considering the transit time. This study considers not only the transportation times of the jobs from one machine to another but also collisions on limited roads, similar to in a real environment. A near-optimal job sequence was obtained by combining the genetic algorithm (GA) with simulation and was comparing this with the traditional method. Additionally, this paper presents heuristics that deal with deadlocks in the process of combining JSSP and simulation. The combination of GA and heuristics was tested using various simulations. © ICROS 2022.

关键词： Genetic algorithms

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High-Frequency Transformer Development for Compact Dual Active Bridge Converters 16

High-Frequency Transformer Development for Compact Dual Acti...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Usolchev, Aleksandr Lukin, Aleksandr Justo, Jackson J. Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Department of Electrical Engineering University of Dar es Salaam Dar es Salaam Tanzania United Republic of

ISBN: (纸本)9798350330885

This paper presents a methodology for designing high-frequency transformers operating at 20 kHz made for operation in dual active bridge converter with a voltage setup of 600V/3x100V. The paper begins by explaining the calculation process for the transformer's design, focusing on crucial aspects such as leakage and magnetizing inductances. Specific techniques for the transformer winding process are discussed, ensuring accuracy and efficiency. Furthermore, the paper analyzed into calculating the parameters that make up the transformer's equivalent circuit, offering insights into its electrical characteristics. It also explores the impact of transformer leakage inductance on the converter's active power performance. To ensure the transformer's robust thermal performance, a thorough thermal analysis using Ansys software is conducted by considering various cooling methods. Finally, the construction details of the transformer, bridging the gap between theoretical design and practical realization are presented. The research concludes with the development of the final transformer construction, harmonizing the theoretical design with practical realization. This study contributes to the field of high-frequency transformer design and highlights the significance of design considerations, particularly in applications where compact and highly efficient power conversion solutions are required. © 2023 IEEE.

关键词： High frequency transformers

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Decentralized Task Assignment for Networked Unmanned Aerial Vehicles

Decentralized Task Assignment for Networked Unmanned Aerial ...

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IEEE Southeastcon

作者： Hava Szarafinski Kiriakos Kiriakidis Department of Weapons Robotics and Control Engineering U.S. Naval Academy Annapolis MD USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Competing with the technical advancements of near-peer adversaries, the Navy is shifting towards increased use of Unmanned Aerial Vehicles (UAVs) to accomplish missions. Formations of networked UAVs are susceptible to single-point failure when assigned tasks centrally, leading to the need for decentralization. This paper investigates the optimal allocation of tasks among networked UAVs by means of distributed algorithms to avoid the single-point vulnerability. Furthermore, it addresses the issue of incomplete information about assignment costs, the result of limited direct connections within the formation. The research synthesizes the Hungarian and consensus classes of algorithms to achieve decentralized task allocation subjected to unknown assignment costs and communications being restricted to immediate neighbors. Within the scope of this paper, the number of tasks and agents are equal and agents know their own costs. Assignment costs of other agent-task pairs are not globally known. The success of the proposed approach is established by demonstrating that the synthesized algorithm reaches the same task assignment compared to centralized optimization using the global cost matrix.

关键词： Costs Runtime Network topology Microcontrollers Autonomous aerial vehicles Robustness Topology Timing Resource management Optimization

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Video Streaming Protocols Comparison in Field Experiments for Aerial Teleoperation 9

Video Streaming Protocols Comparison in Field Experiments fo...

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9th International Conference on Mechanical engineering and robotics Research, ICMERR 2025

作者： Fernandez, Manuel J. Franceschini, Riccardo Becerra, Julian Cayero Fumagalli, Matteo Technical University of Denmark Department of Electrical Engineering Automation and Control Kongens Lyngby2800 Denmark Robotics and Automation Unit Eurecat Technology Center of Catalunya Cerdanyola Del Vallès Barcelona Spain

ISBN: (纸本)9798331509262

As aerial robots evolve to perform complex tasks such as manipulation and visual inspection, maintaining the pilot at the center of decision-making is critical for mission safety and leveraging human expertise. A key enabler of this collaboration is reliable video streaming between the robot and the operator. This paper presents a comparative analysis of four video streaming protocols RTP (Real-time Transport Protocol), RTSP (Real-Time Streaming Protocol), SRT (Secure Reliable Transport), and ROSTCP (ROS Transport control Protocol) based on extensive field experiments. The evaluation focuses on CPU usage, network throughput, latency, streaming quality, and packet integrity across varying distances. Tools such as iperf, ping, GStreamer, and Wireshark were employed for detailed performance measurements. Results reveal significant differences: SRT and RTP deliver superior network stability and resilience to packet loss, while RTSP and ROSTCP show advantages in CPU efficiency. These findings provide actionable insights for optimizing video streaming in aerial robotics, emphasizing the trade-offs and advantages of each protocol in real-world scenarios. © 2025 IEEE.

关键词： Social robots

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Task Load Based Persistent Coverage control for Large-Scale Domains Using Multi-Agent Systems with Anisotropic Sensors

Task Load Based Persistent Coverage Control for Large-Scale ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Odoy, Kyle Rodríguez-Seda, Erick J. United States Naval Academy Department of Weapons Robotics and Control Engineering AnnapolisMD United States

ISBN: (纸本)9798331504847

Multi-agent coverage control refers to the task of coordinating autonomous robots to effectively survey and gather information across a task-domain. This paper introduces a novel approach that addresses agents equipped with anisotropic sensors and constrained by nonholonomic movement, aiming to ensure persistent coverage and maintain awareness over a large-scale domain. The approach employs redefined Voronoi partitions that leverage each agent's local coverage domain based on its relative task load. The task load herein refers to the level of awareness that each agent has about the domain and the size of its area of dominance, all indirectly encoded in the Voronoi's mass. To account for the vehicles' nonholonomic nature and velocity constraints, the proposed approach is extended using the minimum curvature-based distance when determining the agent's area of dominance. A set of simulations involving teams of three and five agents showcases the effectiveness of the proposed control approach. © 2025 IEEE.

关键词： Cloud computing

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Global Navigation Satellite System (GNSS) Emulator for Test and Evaluation of Flight controller Performance

Global Navigation Satellite System (GNSS) Emulator for Test ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Matthew McClelland Zachary Cohen Michael D. M. Kutzer Levi DeVries Robotics & Control Engineering Department United States Naval Academy Annapolis Maryland United States of America

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Global navigation satellite system (GNSS) receivers have become ubiquitous geopositioning sensors in unmanned aerial, ground, and surface systems (UxS). GNSS require line of sight communication with orbiting satellites and the resulting measurement's precision and accuracy can be greatly affected by satellite geometry, atmospheric conditions, and obstructions from buildings or foliage, among other effects. These uncertainties can be difficult to manipulate for test and evaluation of a small-scale UAS control system's robustness to GNSS uncertainty. This work presents the implementation of a GNSS emulator with the same interface design as a GNSS receiver. Using a Raspberry Pi connected wirelessly to a local positioning source, we provide a plug-and-play alternative to a standard commercial-off-the-shelf (COTS) GNSS unit that communicates using the DroneCAN protocol. This system allows the user to simulate GNSS measurements and GNSS performance changes by generating synthetic measurements in a controlled laboratory setting. Data collected from outdoor flights in four different environments is used to characterize baseline GNSS message parameter values, which quantify the fix quality in different geographic locations. This information is used to generate synthetic GNSS measurements fed to a Cube autopilot running ArduCopter flight control software in a hardware in the loop simulation. Results show the GNSS emulator can send DroneCAN GNSS messages providing position and fix quality information to the flight controller. These results illustrate how the plug-and-play GNSS emulator can enable test and evaluation of flight controller robustness to uncertainties, signal dropout, and other conditions affecting GNSS measurements in a controlled laboratory environment.

关键词： Global navigation satellite system Uncertainty Satellites Atmospheric measurements Receivers Extraterrestrial measurements Robustness Software measurement Standards Aerospace control

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Generating Continuous Paths On Learned Constraint Manifolds Using Policy Search

Generating Continuous Paths On Learned Constraint Manifolds ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Ethan Canzini Simon Pope Ashutosh Tiwari Department of Automatic Control & Systems Engineering University of Sheffield UK Airbus Robotics Airbus UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Many robotic manipulation tasks are constrained due to kinematic limitations placed on the object being manipulated. This increases the complexity of manipulation tasks that operate in high dimensions, leading to increased risk that sampling based planners are unable to find optimal solutions. Whilst trajectory optimisation methods provide guaranteed optimal solutions when implementing constraints, they only provide locally optimal solutions in sequential decision-making and struggle to provide globally optimal paths. These constraints can be incorporated into the probabilistic latent spaces by using demonstrations that satisfy the constraint function. However whenever constraints change or a manipulator must perform different tasks the network must be retrained to accommodate the new constraints. In this paper, we provide an approach that allows the training of a single learned manifold that can be augmented to determine the constraint manifold for the manipulation task. Using this manifold, the geodesic between two points can be computed using policy search to solve the cost function associated with the geodesic curve length ${\mathcal{L}_\gamma }$. We provide comparisons in terms of path length against popular path planning algorithms with different kinematic constraints, demonstrating our method’s ability to find optimal shortest paths on constraint manifolds.

关键词： Manifolds Training Runtime Optimization methods Kinematics Reinforcement learning Probabilistic logic Real-time systems Trajectory Splines (mathematics)

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