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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Li, Xinyu Xia, Liang Feng, Wei Chen, Yidong Huang, Shijun Gong, Liming Sun, Tianfu Yanshan University School of Mechanical Engineering Qinhuangdao China Chongqing Robotics Institute Chongqing China Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen Zhaowei Machinery & Electronics Co..Ltd. Shenzhen China Midea Group Corporate Research Center Motors and Drives Center Foshan China

ISBN: (纸本)9798350351330

The Active Disturbance Rejection Controller (ADRC) stands out for its independence from precise mathematical models of controlled systems, offering attributes like minimal overshoot and robustness. However, its incorporation of nonlinear functions complicates parameter adjustment. To address this, linear ADRC (LADRC) emerges as a linearized variant. In this paper, we explore the application of LADRC theory in controlling Permanent Magnet Synchronous Motor servo systems. Specifically, controllers for the current loop, speed loop, and position loop are designed and compared. Simulation results demonstrate that the LADRC-based servo system exhibits superior dynamic and steady-state performance, robustness against interference, and simplified tuning procedures. © 2024 IEEE.

关键词： Servomechanisms

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Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding

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arXiv 2024年

作者： Jiang, He Wang, Yutong Veerapaneni, Rishi Duhan, Tanishq Sartoretti, Guillaume Li, Jiaoyang The Robotics Institute Carnegie Mellon University United States The Department of Mechanical Engineering National University of Singapore Singapore

Lifelong Multi-Agent Path Finding (LMAPF) is a variant of MAPF where agents are continually assigned new goals, necessitating frequent re-planning to accommodate these dynamic changes. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local observations. However, it is still challenging for them to match the performance of the best search-based algorithms, especially in large-scale settings. This work proposes an imitation-learning-based LMAPF solver that introduces a novel communication module and systematic single-step collision resolution and global guidance techniques. Our proposed solver, Scalable Imitation Learning for LMAPF (SILLM), inherits the fast reasoning speed of learning-based methods and the high solution quality of search-based methods with the help of modern GPUs. Across six large-scale maps with up to 10,000 agents and varying obstacle structures, SILLM surpasses the best learning- and search-based baselines, achieving average throughput improvements of 137.7% and 16.0%, respectively. Furthermore, SILLM also beats the winning solution of the 2023 League of Robot Runners, an international LMAPF competition sponsored by Amazon robotics. Finally, we validated SILLM with 10 real robots and 100 virtual robots in a mockup warehouse environment. © 2024, CC BY.

关键词： Multi agent systems

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Dual-modal Motion of Magnetically Controlled Soft Microrobots with Environmentally Responsive Reversible Deformation 24

Dual-modal Motion of Magnetically Controlled Soft Microrobot...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Xin, Zhengyuan Wu, Anping Bai, Kailun Yang, Haotian Lin, Kaijun Shi, Qing Wang, Huaping Hou, Yaozhen Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400710681

Soft microrobots have garnered attention in the biomedical field due to their ability to access hard-to-reach regions within living organisms for precise manipulation. However, the inability to alter the stable morphology after design and fabrication remains a pressing issue for existing soft microrobots. This limitation results in a fixed range of locomotion modes, thereby restricting the diversity of operational environments. Addressing this challenge, this study proposes and designs a biocompatible X-shaped soft microrobot that achieves alterable stable morphologies through an innovative mechanism. We developed a deformable soft hydrogel robot based on alginate gel’s ability to reversibly swell in response to ion concentration changes. Based on the deformation characteristics of this microrobot, we have designed undulating and rolling locomotion modes that are suitable for different morphologies, along with their corresponding magnetic field manipulation systems, enabling multi-morphology locomotion of the robot. Experimental validations demonstrate that the designed flexible microrobot can achieve a deformation deflection angle of up to 150° under manipulation. This robot not only performs forward undulating locomotion in its expanded state but also executes rolling motion in its spherical configuration, exhibiting high locomotion flexibility and adaptability. © 2024 Copyright held by the owner/author(s).

关键词： Microrobots

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Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model

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Artificial Intelligence in Agriculture 2023年第3期9卷 36-45页

作者： Yangyang Guo Samuel E.Aggrey Xiao Yang Adelumola Oladeinde Yongliang Qiao Lilong Chai School of Internet Anhui UniversityHefeiAnhui 230039China Department of Poultry Science University of GeorgiaAthensGA 30602USA U.S.National Poultry Research Center USDA-ARSAthensGA 30605USA Australian Centre for Field Robotics(ACFR) Faculty of EngineeringThe University of SydneyNSW 2006Australia

For commercial broiler production,about 20,000–30,000 birds are raised in each confined house,which has caused growing public concerns on animal ***,daily evaluation of broiler wellbeing and growth is conducted manually,which is labor-intensive and subjectively subject to human ***,there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare *** this study,we developed a YOLOv5-CBAM-broiler model and tested its performance for detecting broilers on litter *** proposed model consisted of two parts:(1)basic YOLOv5 model for bird or broiler feature extraction and object detection;and(2)the convolutional block attention module(CBAM)to improve the feature extraction capability of the network and the problem of missed detection of occluded targets and small targets.A complex dataset of broiler chicken images at different ages,multiple pens and scenes(fresh litter versus reused litter)was constructed to evaluate the effectiveness of the new *** addition,the model was compared to the Faster R-CNN,SSD,YOLOv3,EfficientDet and YOLOv5 *** results demonstrate that the precision,recall,F1 score and an mAP@0.5 of the proposed method were 97.3%,92.3%,94.7%,and 96.5%,which were superior to the comparison *** addition,comparing the detection effects in different scenes,the YOLOv5-CBAM model was still better than the comparison ***,the proposed YOLOv5-CBAM-broiler model can achieve real-time accurate and fast target detection and provide technical support for the management and monitoring of birds in commercial broiler houses.

关键词： Poultry production Deep learning YOLOv5 Attention mechanism

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BrainCLIP: Brain Representation via CLIP for Generic Natural Visual Stimulus Decoding

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Ma, Yongqiang Liu, Yulong Chen, Liangjun Zhu, Guibo Chen, Badong Zheng, Nanning Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Shaanxi Xi'an710049 China Institute of Automation Beijing China

Functional Magnetic Resonance Imaging (fMRI) presents challenges due to limited paired samples and low signal-to-noise ratios, particularly in tasks involving reconstructing natural images or decoding their semantic content. To address these challenges, we introduce BrainCLIP, an innovative fMRI-based brain decoding model. BrainCLIP leverages Contrastive Language-Image Pre-training's (CLIP) cross-modal generalization abilities to bridge brain activity, images, and text for the first time. Our experiments demonstrate CLIP's effectiveness in diverse brain decoding tasks, including zero-shot visual category decoding, fMRI-image/text alignment, and fMRI-to-image generation. The core objective of BrainCLIP is to train a mapping network that translates fMRI patterns into a unified CLIP embedding space, achieved through visual and textual supervision integration. Our experiments highlight that this approach significantly enhances performance in tasks such as fMRI-text alignment and fMRI-based image generation. Notably, BrainCLIP surpasses BraVL, a recent multi-modal method, in zero-shot visual category decoding. Moreover, BrainCLIP demonstrates strong capability in reconstructing visual stimuli with high semantic fidelity, competing favorably with state-of-the-art methods in capturing high-level semantic features during fMRI-based natural image reconstruction. © 1982-2012 IEEE.

关键词： Signal to noise ratio

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Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

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Computer Modeling in engineering & Sciences 2023年第12期137卷 2881-2899页

作者： Zekai Li Wenfeng Wang School of Aerospace Engineering Xi’an Jiaotong UniversityXi’an710049China Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of TechnologyShanghai201418China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi830011China Applied Nonlinear Science Lab Anand International College of EngineeringJaipur391320India Department of Visual Engineering International Academy of Visual Art and EngineeringLondonCR26EQUK Institute of Intelligent Management and Technology Sino-Indian Joint Research Center of AI and RoboticsBhubaneswar752054India

We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge-to learn damaged objects in aerial ***-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated *** the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning *** study results indicated that the object classification accuracy is up to 90%and the average time cost in damage detection is only 0.277 ***,the broad federated meta-learning system is efficient and effective in detecting damaged objects in aerial videos.

关键词： Fuzzy learning system mixed-reality 3D-reconstructed oblique photography

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Formation Control and Sub-Swarm Generation of Multirotor UAVs

Formation Control and Sub-Swarm Generation of Multirotor UAV...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Muhammad Muzamal Shahzad Muhammad Haroon Asad Muhammad Haris Hammad Munawar Muhammad Haroon Yousaf Swarm Robotics Lab National Center of Robotics and Automation University of Engineering and Technology Taxila Taxila Pakistan Computer Engineering Department University of Engineering and Technology Taxila Taxila Pakistan

The research field of collective behaviors takes inspiration from natural self-organizing systems like honeybees, fish schools, social insects, bird flocks, and other social animals. These behaviors can be transformed into robots by replicating the same set of rules as found in the natural swarms. The deployment of aerial swarm robotics has become significant due to their multiple applications in surveillance, agriculture, and military. These applications lead to the development of aerial swarm robotic platforms to solve real-world problems. For example, a swarm of low-tech, cost-effective small UAVs can engage a high-tech target effectively. Similarly, a swarm of UAVs can perform surveillance and rescue missions in disastrous areas and build an emergency communication network through multiple UAV swarms. The critical part of swarm technology is multiple swarm intelligence algorithms that have been proposed and tested by various simulation platforms such as V-rep, Gazebo and MATLAB. Very few of them have been deployed on hardware to solve real scenarios that include formation control, navigation, pattern formation, and a few sub-swarm generation algorithms. Team connectivity is one of the essential aspects of a swarm network that emerges the collective intelligence of the group and ensures the readiness of the algorithm to deploy and solve real-world problems. Many researchers proposed the team connectivity algorithms in a single swarm network but still left the connectivity gap between the sub-swarms during a mission. This research work contributes toward designing a swarm intelligence algorithm to improve the connectivity techniques and team connectivity between the swarm agents and the sub-swarm groups.

关键词： Pattern formation Service robots Navigation Surveillance Insects Swarm robotics Formation control

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American institute of Mathematical Sciences. All rights reserved.

关键词： Empirical mode decomposition

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Learning Rat-Like Behavior for a Small-Scale Biomimetic Robot

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engineering 2022年第10期17卷 232-243页

作者： Zihang Gao Guanglu Jia Hongzhao Xie Qiang Huang Toshio Fukuda Qing Shi Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China

Existing biomimetic robots can perform some basic rat-like movement primitives(MPs)and simple behavior with stiff combinations of these *** mimic typical rat behavior with high similarity,we propose parameterizing the behavior using a probabilistic model and movement ***,an analysis of fifteen 10 min video sequences revealed that an actual rat has six typical behaviors in the open field,and each kind of behavior contains different bio-inspired combinations of eight *** used the softmax classifier to obtain the behavior-movement hierarchical probability ***,we specified the MPs using movement parameters that are static and *** obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means clustering,*** predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series,and the joint trajectory was generalized using a back propagation neural network with two hidden ***,the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands,*** implemented the six typical behaviors on the robot,and the results show high similarity when compared with the behaviors of actual rats.

关键词： Biomimetic Bio-inspired robot Neural network learning system Behavior generation

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Benefits of Internet of Things (IoT) Applications in Health care - An Overview 1

Benefits of Internet of Things (IoT) Applications in Health ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Nicosia Artificial Intelligence Engineering Dept. Mersin10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty OfEngineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

In a nutshell, the IoT refers to the idea of integrating high-tech technology in the form of "smart"gadgets and sensors into a single setting to gather data. There are still numerous areas in which machines cannot compete favorably with people, but data collection and analysis are undeniably two of their strongest points. The Internet of Things makes it possible to fully or partially automation data, it involves related activities, which is obviously very important for the healthcare industry. What exactly is the Internet connected medical things, and how exactly might it make a significant impact on the sector? By having to apply the concept of the internet of things (IoT) to the field of medicine, several significant advancements have become possible, including the ability to automatically control the thermostat of containers while transmitting inoculations and antibiotics, the accurate tracking of the ailments of a lot of illnesses without a need for additional meetings with a general practitioner, the correct and most useful use of meds, and a great deal more. All of this is realizable because of the capability of MedTech gadgets to carry out continuous monitoring system. The potential applications of internet of things technology in healthcare are very interesting. Let's review the ways in which this technology. © 2023 IEEE.

关键词： Medical applications

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