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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Lim, Na-Young Lee, Jeon-Hyeok Park, Tae-Hyoung Chungbuk National University Department of Intelligent Systems & Robotics Cheongju Korea Republic of Chungbuk National University Department of Control & Robot Engineering Cheongju Korea Republic of

ISBN: (纸本)9798331531614

In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range perception. However, current 3D LiDAR object detection methods face challenges with high computational costs and limited detection ranges. These issues make them unsuitable for racing scenarios. To address these challenges, this paper proposes a clustering-based long-range vehicle detection method that relies solely on LiDAR. First, the method removes ground points and foreground points and clusters the remaining points. Subsequently, these clusters are classified as vehicles using AdaBoost, generating 2D bounding boxes in the range view. Experimental results demonstrate superior performance, achieving a computational efficiency of 53 Hz and a long-range detection accuracy of over 80%, compared to voxel-based and range-based methods. This approach offers a viable solution for autonomous racing environments. © 2025 IEEE.

关键词： Autonomous vehicles

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Jogeshwar, Bhaavin Kishore Bohannon, Joshua Sevil, Hakki Erhan Department of Intelligent Systems and Robotics PensacolaFL32514 United States Department of Electrical and Computer Engineering PensacolaFL32514 United States

ISBN: (纸本)9798331504847

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies. © 2025 IEEE.

关键词： Object detection

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A Machine Learning-Driven Model for Predicting the Productivity of Internet of Mechanical Things 2nd

A Machine Learning-Driven Model for Predicting the Productiv...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Sedik, Ahmed Nasralla, Moustafa M. Esmail, Maged Abdullah Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

ISBN: (纸本)9789819783472

This study delves into the utilization of artificial intelligence (AI) for predicting the performance of Hemispherical Solar Stills (HSS), aiming to transcend reliance on empirical methods. Employing real-world experimental data, five distinct prediction models were developed. The investigation revealed that the DT model emerged as the most efficacious in estimating both hourly productivity and instantaneous efficiency, showcasing remarkable accuracy and efficiency in its predictions. Statistical metrics demonstrated that the DT model's estimates for HSS productivity and efficiency were closely aligned with unity. This research accentuates the transformative potential of AI models in streamlining and enhancing predictive techniques for estimating HSS performance. The findings advocate for the adoption of AI-driven approaches as cost-effective and precise solutions for researchers and practitioners within the realm of mechanical engineering. By substantiating the efficacy of the DT model, this study contributes valuable insights that not only advance our understanding of AI applications in solar technology but also offer practical tools for optimizing the design and performance assessment of Hemispherical Solar Stills. The integration of AI in this context promises to usher in a new era of efficiency and accuracy, addressing challenges in the field and fostering advancements in sustainable energy technologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Machine Learning-based Optimal Trajectory Planning for Spacecraft Passively Flying Around a Satellite for Proximity Operations

Machine Learning-based Optimal Trajectory Planning for Space...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Barth, Andrew Roach, Dylan Ma, Ou Hernandez, Michael Ashbaugh, Ryan Fleming, Alex Department of Aerospace Engineering Intelligent Robotics and Autonomous Systems Laboratory University of Cincinnati CincinnatiOH45221 United States iMETALX Inc SausalitoCA94965 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

In-space or On-orbit Servicing, Assembly, and Manufacturing (ISAM or OSAM) activities require spacecraft to fly around a client satellite or another orbiting object in a proximity range from 100’s of meters to 0 meters in distance. When performing such proximity operations, it becomes extremely important for the servicing craft to not only avoid collision but at the same time fly close enough for better situational awareness and minimal fuel consumption. This is especially important because such orbiting spacecraft will often be low-cost CubeSats performing OSAM tasks, therefore, planning a Passive Relative Orbit (PRO) becomes essential for low-risk and energy-efficient operations. Next, the problem of how to deploy a CubeSat from a station-keeping Chief spacecraft along a transfer trajectory, followed by a PRO-based two-impulse burn maneuver with minimal fuel consumption and variable time constraints, needs to be solved. Once the task is completed, another two-burn transfer trajectory will be calculated for the servicing deputy to return to the Chief. In this paper, we use a machine learning approach to determine the ΔV burns required to achieve the relative transfer and task trajectories. Machine learning allows us to improve our mission planning capabilities and gives the system more freedom to make choices where the operator does not have clear or full insight of the complex physics under many constraints. The system was trained using the proximal policy optimization algorithm for a range of task trajectory parameters randomly selected for each training episode. The results of the trained system were validated by comparison to those produced by traditional optimization methods from our previous work. The robustness of the trained algorithm was verified by 1,000 random scenarios falling within the range of trained conditions. The study demonstrates the ability to use the trained model for a variety of situations without the need to retrain specially for each type

关键词： Orbits

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Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic systems, Measurement and Control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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Adaptive Closed-Loop Functional Electrical Stimulation System With Visual Feedback for Enhanced Grasping in Neurological Impairments

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 678-686页

作者： Lin, Chengyu Cheng, Kong Hoi Pan, Wei Sun, Jinxin Gou, Guotao Fu, Junyun Leng, Yuquan Fu, Chenglong Southern University of Science and Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities Department of Mechanical and Energy Engineering Shenzhen518055 China Guilin University of Electronic Technology Guangxi Human Physiological Information Non Invasive Detection Engineering Technology Research Center School of Life and Environmental Sciences Guilin541004 China

Grasping is a critical motor skill essential for daily activities, but it is often compromised in individuals with neural impairments. Functional Electrical Stimulation (FES) has emerged as a promising intervention, utilizing electrical pulses to stimulate muscles and thereby restore impaired motor functions. However, existing closed-loop FES systems depend on pre-calibrated angles or forces specific to individual objects, which limits their practicality in dynamic, real-world environments with varying object properties. This paper presents a novel closed-loop FES (CLFES) system with visual feedback, designed to dynamically adjust stimulation parameters based on real-time interaction states without requiring object-specific calibration. The system employs a finite state machine to manage sequential grasp-release tasks and integrates a visual perception module for slip detection and intent recognition. The system was tested with two individuals with disabilities on five common household objects. Experimental results demonstrate significant improvements, including a 42.6% increase in success rate and a 45.9% reduction in task completion time compared to tasks performed without the system. These results underscore the system's potential to improve daily task performance for individuals with neural impairments. © 2018 IEEE.

关键词： Visual servoing

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Impact of EV charging strategies on solar-powered residential buildings: unidirectional vs. bidirectional charging in Jordan

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Discover Sustainability 2025年第1期6卷 1-16页

作者： Al-Amayreh, Malik Alamayreh, Ahmad Amer, Amal A. Alternative Energy Technology Department Faculty of Engineering and Technology Al-Zaytoonah University of Jordan P.O. Box 130 Amman 11733 Jordan Department of Intelligent Robotics Systems Engineering American University of Madaba Amman Jordan Data Science and Artificial Intelligence Department Al-Ahliyya Amman University Amman Jordan

Unidirectional chargers, valued for their simplicity and cost-effectiveness, are widely deployed. In contrast, bidirectional chargers enable advanced functionalities such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) but come with greater complexity, higher costs, and design challenges. This aim of this research is to analyze unidirectional and bidirectional charging systems integrated with renewable energy, from both economic and environmental perspectives. Additionally, the research conducts a technical analysis of different EV charging technologies via Polysun software, considering a predefined mobility profile that includes charging times and kilometers driven. The study focuses on households with renewable energy systems connected to the grid, evaluating energy consumption, grid reliance, CO₂ emissions, and financial viability across scenarios with varying numbers of EVs (1–3) over one year. While bidirectional EV setups enhance self-consumption and reduce dependence on the external grid, they face financial challenges, including higher initial costs and a lower net present value (NPV) due to maintenance expenses. In Jordan the time-of-use (TOU) pricing system has applied for EVs charging. This study reveals that the bidirectional EV charging improves energy efficiency and reduces CO₂ emissions by optimizing PV energy utilization in Jordan to charge EVs, however, its increased initial costs under TOU pricing highlight the need for supportive policies to encourage wider adoption. © The Author(s) 2025.

关键词： Bidirectional charging CO₂ emissions in EV charging Solar-powered EV charging Time of use pricing Unidirectional charging

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Model Predictive Torque Control of Six-Phase Switched Reluctance Motors Based on Improved Voltage Vector Strategy

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7650-7661页

作者： Yang, Yifei Sun, Xiaodong Dianov, Anton Demidova, Galina Prakht, Vladimir Wang, Yong Han, Shouyi Suzhou Vocational University School of Mechanical and Electrical Engineering Suzhou215104 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China

To rapidly and accurately establish the model of switched reluctance motors (SRMs) and enhance torque control performance, this paper proposes a model predictive torque control (MPTC) strategy based on the optimized voltage vector. First, a fourth-order Fourier series is used to calculate the flux and torque models, and a complete nonlinear model of the SRM is constructed using the Kriging model. Second, the torque characteristics are used to divide the sectors, thereby reducing the number of candidate voltage vectors (CVVs) and effectively decreasing the computational burden of predictive control. Finally, an adaptive adjustment algorithm for the sector boundary angle is proposed, where the position of the sectors is determined based on variations in speed and load. The proposed method reduces torque ripple and enhances dynamic response capability. The effectiveness of this approach is validated through experiments on a 12/10 pole six-phase SRM prototype. © 2015 IEEE.

关键词： Vectors

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Mechanical Properties Inside Origami-Inspired Structures: An Overview

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Applied Mechanics Reviews 2025年第1期77卷 011001页

作者： Yan, Peng Huang, Hailin Meloni, Marco Li, Bing Cai, Jianguo Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China Harbin Institute of Technology Shenzhen518052 China Key Laboratory of C & PC Structures of Ministry of Education National Prestress Engineering Research Center Southeast University Nanjing211189 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China Key University Laboratory of Mechanism Machine Theory and Intelligent Unmanned Systems of Guangdong Habin Institute of Technology Shenzhen518052 China

In recent decades, origami has transitioned from a traditional art form into a systematic field of scientific inquiry, characterized by attributes such as high foldability, lightweight frameworks, diverse deformation modes, and limited degrees-of-freedom. Despite the abundant literature on smart materials, actuation methods, design principles, and manufacturing techniques, comprehensive reviews focusing on the mechanical properties of origami-inspired structures are relatively rare and unsystematic. This review aims to fill this void by analyzing and summarizing the significant studies conducted on the mechanical properties of origami-inspired structures from 2013 to 2023. We begin with an overview that includes essential definitions of origami, classical origami patterns, and their associated tessellated or stacked structures. Following this, we delve into the principal dynamic modeling method for origami and conduct an in-depth analysis of the key mechanical properties of origami-inspired structures. These properties include tunable stiffness, bistability and multistability, metamechanical properties demonstrated by origami-based metamaterials, and bio-inspired mechanical properties. Finally, we conclude with a comprehensive summary that discusses the current challenges and future directions in the field of origami-inspired structures. Our review provides a thorough synthesis of both the mechanical properties and practical applications of origami-inspired structures, aiming to serve as a reference and stimulate further research. © 2025 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Degrees of freedom (mechanics)

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