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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Advances of triboelectric and piezoelectric nanogenerators toward continuous monitoring and multimodal applications in the new era

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International Journal of Extreme Manufacturing 2025年第1期7卷 174-205页

作者： Jianlong Hong Xiao Wei Huiyun Zhang Yukun Xiao Chongguang Meng Yuqi Chen Jiahui Li Ling Li Sanghoon Lee Qiongfeng Shi Jun Wu Interdisciplinary Research Center School of Electronic Science and EngineeringSoutheast UniversityNanjing 211189People’s Republic of China Department of Robotics&Mechatronics Engineering Daegu Gyeongbuk Institute of Science&TechnologyDaegu 42899Republic of Korea

Benefiting from the widespread potential applications in the era of the Internet of Thing and metaverse,triboelectric and piezoelectric nanogenerators(TENG&PENG)have attracted considerably increasing *** outstanding characteristics,such as self-powered ability,high output performance,integration compatibility,cost-effectiveness,simple configurations,and versatile operation modes,could effectively expand the lifetime of vastly distributed wearable,implantable,and environmental devices,eventually achieving self-sustainable,maintenance-free,and reliable ***,current triboelectric/piezoelectric based active(***-powered)sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient *** work systematically summarizes and evaluates the recent research endeavors to address the above challenges,with detailed discussions on the challenge origins,designing strategies,device performance,and corresponding diverse ***,conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided,proposing the necessity of future research development in this field.

关键词： triboelectric piezoelectric continuous monitoring multimodal sensing internet of thing

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Decomposition and Coordination-Based Iterative Identification for a Class of Separable Nonlinear Models 16th

Decomposition and Coordination-Based Iterative Identificati...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zhou, Yihong Liu, Qinyao Yang, Dan School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Department of Mechatronics and Robotics Xi’an Jiaotong Liverpool University Suzhou215123 China Department of Electromechanical Engineering Changzhou Vocational Institute of Textile and Garment Changzhou213164 China

ISBN: (纸本)9789819617760

This paper studies the parameter estimation problem for a class of separable nonlinear models, i.e., the RBF-ARX models. By exploiting the parameter separability property inherent in the RBF-ARX models, two gradient-based iterative sub-algorithms are derived to individually estimate the linear and nonlinear parameters based on the iterative search. Then a decomposition coordination-based iterative algorithm is proposed by integrating the sub-algorithms, which realizes high-precision iterative identification of all parameters. The effectiveness of the proposed algorithm is verified by a simulation example. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nonlinear analysis

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Machine-learning-driven prediction of flow curves and development of processing maps for hot-deformed Ni–Cu–Co–Ti–Ta alloy

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Journal of Materials Research and technology 2025年 36卷 7447-7456页

作者： Reliance Jain Sandeep Jain Sheetal Kumar Dewangan M.R. Rahul Sumanta Samal Eunhyo Song Younggeon Lee Yongho Jeon Krishanu Biswas Gandham Phanikumar Byungmin Ahn Department of Materials Science and Engineering Ajou University Suwon 16499 Republic of Korea Department of Mechanical Engineering Ajou University Suwon 16499 Republic of Korea Department of Automation & Robotics Engineering Prestige Institute of Engineering Management and Research Indore 452010 India School of Materials Science and Engineering Yeungnam University Gyeongsan 38541 Republic of Korea Department of Fuel Minerals and Metallurgical Engineering Indian Institute of Technology (ISM) Dhanbad 826004 India Department of Metallurgy Engineering and Materials Science Indian Institute of Technology Indore Indore 453552 India Department of Energy Systems Research Ajou University Suwon 16499 Republic of Korea Department of Materials Science and Engineering Indian Institute of Technology Kanpur Kanpur 208016 India Department of Metallurgical and Materials Engineering Indian Institute of Technology Madras Chennai 600036 India

Optimizing hot deformation conditions is critical for achieving efficient thermo-mechanical processing of advanced alloy systems. In this study, a multicomponent Ni 48 Cu 10 Co 2 Ti 38 Ta 2 alloy was developed, exhibiting a refined eutectic microstructure composed of NiTi and Ni 3 Ti phases, along with coarse Ti 2 Ni and NiTi dendritic phases. High-temperature compression tests were performed using a Gleeble® thermo-mechanical simulator over a temperature range of 973–1273 K and strain rates from 10 −2 to 10 s −1 to investigate the alloy flow behavior. To reduce experimental efforts and enhance prediction accuracy, five machine learning (ML) models random Forest (RF), XGBoost (XGB), decision tree (DT), K-Nearest neighbor (KNN), and gradient boosting (GB) were applied to predict the flow stress–strain response and construct processing maps. Among these, the RF model demonstrated superior predictive performance, particularly at a strain rate of 0.1 s −1 , with R 2 = 0.97, RMSE = 10.1 %, and MAE = 8.9 %. The flow curves predicted by the RF model were used to develop precise processing maps, identifying optimal and safe deformation conditions. The resulting processing maps were validated through experiments, confirming that the alloy can be safely deformed within the temperature range of 1173–1273 K and strain rates between 10 −0.8 and 10 −2 s −1 . This integrated experimental–computational approach offers a reliable and efficient strategy for determining hot working conditions, reducing material and energy consumption. It also presents a robust framework for advancing the development of high-temperature alloy systems through the combination of ML techniques and experimental validation.

关键词： Ni–Ti-Based alloy Hot deformation Kinetic analysis Processing map Machine learning

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A Novel Tactile Sensing Skin for Surface Perception

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IEEE Sensors Journal 2025年

作者： Das, Subham Thiyagarajan, Karthick Kodagoda, Sarath Krishnan, Athul Bhattacharjee, Mitradip Indian Institute of Science Education and Research Bhopal i-Labs Electrical Engineering and Computer Science Department Madhya Pradesh 462066 India Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment PenrithNSW2751 Australia UTS Robotics Institute University of Technology Sydney Australia SydneyNSW2007 Australia

Humans employ a fusion of pressure and temperature signals to perceive tactile stimuli. While replicating the complexity of human tactile sensing poses challenges, advancements in artificial tactile sensing skins play a crucial role in the progress of robotics and prosthetics. Here, we present a novel tactile sensing skin designed for the perception of diverse surfaces. It utilizes a microstructured porous tactile sensor, exhibiting notable sensitivity at 21.5 kPa-1 under low-pressure conditions and 2.5 kPa-1 in high-pressure scenarios. The sensor also boasts an impressively low detection limit of 2.5 Pa, allowing for discernment of subtle pressure variations. Simultaneously, we utilize a flexible temperature sensor exhibiting a sensitivity of 0.58 % ∘C-1 and swift response attributes to facilitate the detection of radiated heat from diverse surfaces. The incorporation of a multilayer perceptron algorithm into the developed glove-like system, equipped with tactile and temperature sensors, achieves 87% accuracy in classifying eleven distinct surfaces. © 2001-2012 IEEE.

关键词： Tactile sensors

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Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling down to 10K in Advanced 7nm FinFETs

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IEEE Transactions on Computers 2025年

作者： Ravipati, Divya Praneetha van Santen, Victor M. Parihar, Shivendra Singh Chauhan, Yogesh Singh Panda, Preeti Ranjan Amrouch, Hussam Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Germany Kanpur Nanolab Department of Electrical Engineering Uttar Pradesh India

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model Cryo-CACTI for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. © 1968-2012 IEEE.

关键词： Combinatorial circuits

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Collaborative Perception Datasets for Autonomous Driving: A Review

arXiv

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arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-Datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

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Damage evolution mechanism and low-damage grinding technology of silicon carbide ceramics

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International Journal of Extreme Manufacturing 2025年第2期 555-591页

作者： Chen Li Kechong Wang Oleg Zakharov Hailong Cui Mingtao Wu Tianchen Zhao Yongda Yan Yanquan Geng State Key Laboratory of Robotics and System (HIT) Harbin Institute of Technology School of Mechatronics Engineering Harbin Institute of Technology Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult-to-cut Material Hunan University of Science and Technology Moscow State University of Technology Sichuan Precision and Ultra-Precision Machining Engineering Technology Center College of Mechanical Engineering Quzhou University

Silicon carbide(SiC) ceramics are extensively utilized in aerospace, national defense, and petrochemical industries due to their superior physical and chemical properties. The processing of bulk SiC ceramics necessitates precise and efficient grinding techniques to produce components with satisfactory functionality. However, the inherent high hardness and brittleness of SiC ceramics present significant challenges during grinding, leading to severe brittle fracture and tool wear that compromise both surface integrity and production efficiency. Although ductile-regime grinding of SiC ceramics can be achieved by enhancing machine tool accuracy and stiffness while optimizing wheel performance alongside appropriate selection of process parameters, a comprehensive summary of the mechanisms underlying damage evolution during grinding is lacking, and a mature grinding process for SiC ceramics has yet to be developed. To bridge this gap, the sintering technologies, mechanical properties, and microstructures of SiC ceramics were briefly covered. The grinding-induced damage mechanism and low-damage grinding technologies of SiC ceramics were summarized. The fundamental science underlying the ductile deformation and removal mechanisms of brittle solids was emphasized. Additionally,attention was directed towards the critical role of hybrid energy field grinding in minimizing brittle damages and promoting removal efficiency. This review not only elucidates the intrinsic interactions between the work material and abrasives, but also offers valuable insights for optimizing the grinding processes of brittle solids.

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Hybrid deep learning model to predict the ultimate tensile strength of friction stir welded joints

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Mishra, Akshansh Miloudi, Abdelkader Sefene, Eyob Messele Arroussi, Chaaben Chekalil, Ismail Muthanna, Bassam Gamal Nasser School of Industrial and Information Engineering Politecnico Di Milano Milan Italy Sidi Bel Abbes Algeria Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan Faculty of Mechanical and Industrial Engineering Bahir Dar Institute of Technology Bahir Dar University P.O. Box 26 Bahir Dar Ethiopia LPTPM Departement of Mechanical Engineering Faculty of Technology Hassiba BenBouali University of Chlef P.O. Box. 151 Hay Salem Chlef02000 Algeria Interdisciplinary Research Center for Intelligent Manufacturing & Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Mechatronics Engineering Faculty of Engineering and Computing University of Science and Technology Aden Yemen

Accurate prediction of tensile strength in friction stir welded joints is essential to improve the reliability of welded structures. Traditional Artificial Neural Networks (ANNs) often struggle to capture the complex relationships between welding parameters, leading to limited predictive accuracy. To address this challenge, a hybrid model was developed, combining Long Short-Term Memory (LSTM) and dense layers to leverage the ability of LSTM to learn sequential patterns and dependencies between inputs such as rotational speed, feed rate, and tool tilt angle. This hybrid model was compared to a traditional ANN model using a dataset containing welding parameters and tensile strength results. Performance indicators, including correlation coefficient and Mean Square Error (MSE), showed that the LSTM + Dense hybrid model achieved a higher correlation coefficient (0.9803) and lower MSE on the validation sets, indicating improved predictive ability. Analyses of error distribution and convergence of training and validation losses also showed that the hybrid model had higher consistency and reliability with reduced overfitting, even when dealing with outliers. The LSTM + Dense model was shown to significantly outperform the traditional ANN, making it a powerful tool for predicting tensile strength in welding applications with complex and nonlinear data. Although the accuracy and reliability of the proposed hybrid model have improved, limitations remain, including the need for larger datasets to enhance generalization and potential challenges in real-time implementation. © 2025 Elsevier Ltd

关键词： Deep neural networks

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