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

作者： Meng Wang Yao Su Hang Li Jiarui Li Jixiang Liang Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BI-GAI) Department of Advanced Manufacturing and Robotics College of Engineering Peking University Department of Robot Engineering School of Automation Science and Electrical Engineering Beihang University

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

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A Comprehensive Analysis of Internet of Things (IOT) Sustained Integrity System Services with Data Management Based ON Machine Learning 2

A Comprehensive Analysis of Internet of Things (IOT) Sustain...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Sekhar, C.H. Meenakshi Mousa, Saeed Sharma, M.K. Gobinath, V.M. Dharamvir Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam India Gd Goenka University HARYANA SOHNA India CLERSE University of Lille for Science and Technology France Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut250004 India Karpaga Vinayaga College of Engineering and Technology Robotics and Automation Tamil Nadu Chengalpattu India The Oxford College of Engineering Department of Mca Bangalore India

ISBN: (纸本)9781665437899

Internet of Things (IoT) and Machine Learning (ML) are recent trends in huge data management. Traditionally, data management was carried out by humans where big data collection, analysis and acquisition were solely done by humans. However, IoT and ML have enabled machines rather than humans to collect big data, analyse them and develop a new business model. It has shown huge success in industry 4.0;however, during the management of big data, large-scale companies encountered certain issues. These include, lack of effective training leads to less accuracy in data management, security issues during cloud computing and security issues in blockchain as well. Therefore, this study has explained the ways to improve big data management through ML and to enhance the security of blockchain. A primary survey was carried out with 50 respondents who provided their responses to certain questions related to big data management solutions and blockchain security. Findings showed that providing training to the workers and using guidelines/policies in blockchain can increase the data management accuracy and can improve the security in cloud computing, respectively. © 2022 IEEE.

关键词： Information management

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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Simulation Study of Control Techniques Used for 9- Phase Voltage Source Inverter

Simulation Study of Control Techniques Used for 9- Phase Vol...

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2021 International Conference on System, Computation, automation and Networking, ICSCAN 2021

作者： Renuka Devi, G. Karthik, C. Malaiyappan, Prakash Avanthikha, A. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Puducherry India Jyothi Engineering College Department of Mechatronics Kerala India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India SSN College of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9781665439862

This paper presents the control techniques used for 9-Phase Voltage Source Inverter. For high power application the 9-phase inverter fed 9-phase drives dominantly used. In the proposed work a comprehensive relationship between the 9-phase SPWM, 9th harmonic injection technique and 9-phase modified SVPWM technique are presented. The simulation study of the 9-phase voltage source inverter is investigated with the above said switching methods with varying modulation indices. The MATLAB /Simulink results are addressed for varying modulation indices and analysis the results in terms of output phase voltage and total harmonic distortion. From the results find the optimum method is appropriate for 9-phase VSI. © 2021 IEEE.

关键词： Electric inverters

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SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autono...

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International Conference on Computer Vision (ICCV)

作者： Yi Wei Linqing Zhao Wenzhao Zheng Zheng Zhu Jie Zhou Jiwen Lu Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China School of Electrical and Information Engineering Tianjin University China PhiGent Robotics

3D scene understanding plays a vital role in vision-based autonomous driving. While most existing methods focus on 3D object detection, they have difficulty describing real-world objects of arbitrary shapes and infinite classes. Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images. We first extract multi-scale features for each image and adopt spatial 2D-3D attention to lift them to the 3D volume space. Then we apply 3D convolutions to progressively upsample the volume features and impose supervision on multiple levels. To obtain dense occupancy prediction, we design a pipeline to generate dense occupancy ground truth without expansive occupancy annotations. Specifically, we fuse multi-frame LiDAR scans of dynamic objects and static scenes separately. Then we adopt Poisson Reconstruction to fill the holes and voxelize the mesh to get dense occupancy labels. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our method. Code and dataset are available at https://***/weiyithu/SurroundOcc.

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Quaternion-Based Attitude Stabilization Using Synergistic Hybrid Feedback With Minimal Potential Functions

arXiv

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arXiv 2024年

作者： Tong, Xin Ding, Qingpeng Fang, Haiyang Cheng, Shing Shin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong CUHK T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong Hong Kong Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong

This paper investigates the robust global attitude stabilization problem for a rigid-body system using quaternion-based feedback. We propose a novel synergistic hybrid feedback with the following notable features: (1) It demonstrates central synergism by utilizing a minimal number of potential functions;(2) It ensures consistency with respect to the unit quaternion representation of rigid-body attitude;(3) Its state-feedback laws incorporate a shared action term that steers the system toward the desired attitude. We demonstrate that the proposed hybrid feedback method effectively solves the problem at hand and guarantees robust uniform global asymptotic stability. © 2024, CC BY-NC-ND.

关键词： Attitude control

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Precise LiDAR SLAM in Structured Scene Using Finite Plane and Prior Constraint

Precise LiDAR SLAM in Structured Scene Using Finite Plane an...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xie, Yuhui Zhao, Wentao Wang, Jiahao Wang, Jingchuan Chen, Weidong Institute of Medical Robotics and Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Ministry of Education Shanghai200240 China

ISBN: (纸本)9781665481090

In this paper, we propose a precise LiDAR SLAM in optimization framework using plane-like object as landmark. Compared to general methods, finite plane feature is used to represent landmark and a new residual model is designed, making constraint from edge of landmark can be used to limit the parallel position between LiDAR and landmark, leading to a more accurate result. Moreover, floor plan is used to provide global pose for reduction of drift, and additionally feature orientation prior is used to prevent map distortion when updating the inaccurate part of floor plan. Experiments are conducted using data collected in real environment. The result qualitatively shows that proposed method can build a corrected map from floor plan without distortion, and quantitatively verifies that proposed method can outperform other baseline methods in accuracy. © 2022 IEEE.

关键词： Optical radar

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A Multipurpose Interface for Close- and Far-Proximity Control of Mobile Collaborative Robots

arXiv

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arXiv 2024年

作者： Raei, Hamidreza Gandarias, Juan M. De Momi, Elena Balatti, Pietro Ajoudani, Arash HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Robotics and Mechatronics lab Systems Engineering and Automation Department University of Malaga Malaga Spain

This letter introduces an innovative visuo-haptic interface to control Mobile Collaborative Robots (MCR). Thanks to a passive detachable mechanism, the interface can be attached/detached from a robot, offering two control modes: local control (attached) and teleoperation (detached). These modes are integrated with a robot whole-body controller and presented in a unified close- and far-proximity control framework for MCR. The earlier introduction of the haptic component in this interface enabled users to execute intricate loco-manipulation tasks via admittance-type control, effectively decoupling task dynamics and enhancing human capabilities. In contrast, this ongoing work proposes a novel design that integrates a visual component. This design utilizes Visual-Inertial Odometry (VIO) for teleoperation, estimating the interface’s pose through stereo cameras and an Inertial Measurement Unit (IMU). The estimated pose serves as the reference for the robot’s end-effector in teleoperation mode. Hence, the interface offers complete flexibility and adaptability, enabling any user to operate an MCR seamlessly without needing expert knowledge. In this letter, we primarily focus on the new visual feature, and first present a performance evaluation of different VIO-based methods for teleoperation. Next, the interface’s usability is analyzed in a home-care application and compared to an alternative designed by a commercial MoCap system. Results show comparable performance in terms of accuracy, completion time, and usability. Nevertheless, the proposed interface is low-cost, poses minimal wearability constraints, and can be used anywhere and anytime without needing external devices or additional equipment, offering a versatile and accessible solution for teleoperation. © 2024, CC BY-NC-ND.

关键词： Remote control

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Synthesis and Characterization of Magnetic Nano Particle Materials (Fe2O3 & Fe3O4) Synthesized by Arc Discharge Method

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Journal of The Institution of Engineers (India): Series D 2025年 1-12页

作者： Junaidi, Md. Abdul Raheem Ranjitha, P. Kumar, K. V. Pradeep Rajanish, M. Reddy, Maramreddy Raghu Tilak Manjulaiah, Hareesha Thanuj Kumar, M. Nagaraj, P. Revanna, K. Raghavendra, N. Udayashankar, S. Mechanical Engineering Department Muffakham Jah College of Engineering & Technology Telangana Hyderabad500034 India Mechanical Engineering Department Dayanand Sagar College of Engineering Karnataka Bangalore560078 India Mechanical Engineering Department Ramaiah Institute of Technology Karnataka Bengaluru560054 India Mechanical Engineering Department Dayananda Sagar Academy of Technology and Management Karnataka Bengaluru560082 India Mechanical Engineering Department New Horizon College of Engineering Karnataka Bangalore560103 India Department of Mechanical Engineering Malnad College of Engineering Hassan573202 India Visvesvaraya Technological University Karnataka Belagavi590018 India Department of Robotics and Automation Rajarajeswari College of Engineering Bengaluru560074 India Department of Chemistry Yenepoya Institute of Technology Karnataka Mangalore574225 India Department of Mechanical Engineering Government Engineering College Krishnarajapet571426 India Department of Mechanical Engineering BNM Institute of Technology Karnataka Bangalore560070 India Department of Mechanical Engineering VTU Karnataka Belgaum590018 India

This research work thoroughly describes the process of obtaining and characterizing iron oxide (Fe3O4) nanoparticles as well as emphasizes how different pH levels, reaction environments and synthesis methods can respectively control its size or activity. Characterization of the crystalline structure, morphology and size of the nanoparticles was carried out. It describes key methods of synthesis such as arc discharge or electrode configuration in electrolyte solutions emphasizing on applied voltage or processing time, which resulted in good quality of nanoparticles, concluding with comparing particle diameters and prepared form of nanoparticles in mentioned methods. In this proposed synthesis mechanism, ferrous and ferric ions are hydroxylated in a controlled anaerobic process to yield Fe3O4 precipitates. Synthesis of the iron oxide (Fe3O4) nanoparticles was found to highly depend on pH parameters, and pH 11 was optimized as the favorable pH parameter based on the results acquired, as it well controls the growth and stability of the particles. The findings highlight the promise of Fe3O4 nanoparticles for various biomedicine and environmental remediation applications where particle stability, magnetic properties and cheap synthesis are crucial. © The Institution of Engineers (India) 2025.

关键词： Magnetic nanoparticles

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Effects of Knee Joint Misalignments on Human-Exoskeleton Interaction Dynamics

Effects of Knee Joint Misalignments on Human-Exoskeleton Int...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Stefano Massardi David Rodriguez-Cianca Juan C. Moreno Matteo Lancini Diego Torricelli BioRobotics group Centre for Automation and Robotics (CAR) Madrid Spain Department of Industrial and Mechanical Engineering (DIMI) University of Brescia Brescia Italy Department of Medical and Surgical Specialties Radiological Sciences and Public Health (DSMC) University of Brescia Brescia Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

One aspect that characterizes exoskeletons is their close physical contact with the user. Physical human-exoskeleton interaction (pHEI) is strongly affected by kinematic incompatibilities generated in the human-exoskeleton kinematic chain, often resulting in undesired joint misalignments and interaction forces. However, a systematic analysis to highlight this relation is difficult to execute. In this work we use a methodology for the study of pHEI based on an active dummy leg named Leg Replica. Thanks to this system, our aim is to highlight the relations between key pHEI metrics such as joint misalignment, interaction forces and relative displacements, underlining the role and effects of joint misalignment in controlled conditions. We first validated a kinematic model available in the literature relating human-exoskeleton knee misalignment with the resulting device's motion relative to the limb. The model was further used to investigate which is the relation between the kinematic of the leg-device system and the forces produced between the two bodies. We discovered that the model predictions were consistent with the experimental kinematic observations and strongly related with the peaks reached by shear forces during the motion. Shear forces were found to play a key role in the overall interaction and related with the lost of energy during the motion. These results underscore the strong influence that misalignments can exert on the generation of force and motion, even under simplified and controlled conditions.

关键词： Legged locomotion Knee Systematics Force Exoskeletons Dynamics Kinematics Predictive models Motion measurement Robots

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