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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Khan, Roohan Ahmed Zafar, Malaika Batool, Amber Fedoseev, Aleksey Tsetserukou, Dzmitry Center for Digital Engineering Skolkovo Institute of Science and Technology Intelligent Space Robotics Laboratory Moscow Russia

ISBN: (纸本)9781665481090

In the area of multi-drone systems, navigating through dynamic environments from start to goal while providing collision-free trajectory and efficient path planning is a significant challenge. To solve this problem, we propose a novel SwarmPath technology that involves the integration of artificial potential field (APF) approach with impedance controller. The proposed approach provides a solution based on collision-free leader-follower behaviour where drones are able to adapt themselves to the environment. Moreover, the leader is virtual while drones are physical followers, leveraging APF path planning approach to find the smallest possible path to the target. Simultaneously, the drones dynamically adjust impedance links, allowing themselves to create virtual links with obstacles to avoid them. As compared to conventional APF, the proposed SwarmPath system not only provides smooth collision avoidance but also enables agents to efficiently pass through narrow passages by reducing the total travel time by 30% while ensuring safety in terms of drone connectivity. Lastly, the results also illustrate that the discrepancies between the simulated and real environments exhibit an average absolute percentage error (APE) of 6% of drone trajectories. This underscores the reliability of our solution in real-world scenarios. Video: https://***/k8Nf_vPZf7U. © 2024 IEEE.

关键词： Drones

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蚯蚓直线运动的三轴地面反作用力:测量、分析和对机器人建模的影响

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Acta Mechanica Sinica 2024年第2期40卷 1-14页

作者：方虹斌师瑞徐鉴 Institute of AI and Robotics State Key Laboratory of Medical NeurobiologyMOE Engineering Research Center of AI&RoboticsFudan UniversityShanghai200433China

蚯蚓在狭窄受限环境中具有良好的移动性,但其在运动过程中与地面的三轴反作用力还未被深入研究.本文探讨了蚯蚓在直线运动过程中的三轴地反力特征及其对机器人建模的影响.具体地,本研究设计了一个基于应变片的三轴微型测力板来测量蚯蚓(... 详细信息

蚯蚓在狭窄受限环境中具有良好的移动性,但其在运动过程中与地面的三轴反作用力还未被深入研究.本文探讨了蚯蚓在直线运动过程中的三轴地反力特征及其对机器人建模的影响.具体地,本研究设计了一个基于应变片的三轴微型测力板来测量蚯蚓(***)在直线运动时的三轴地反力.通过建立地反力的时间历程与运动视频的对应关系,将蚯蚓的周期性运动划分为三个阶段,并从蚯蚓身体变形的角度解析了地反力在三个阶段的力学特征.本研究还对蚯蚓的地反力特征进行了统计分析,并建立了蚯蚓直线运动过程的阻力系数模型.以此为基础,本文还提出了与机器人节段/单元变形相关的阻力系数模型,为离散型和连续型仿蚯蚓机器人的动力学建模提供基础.本研究首次对蚯蚓运动过程中的mN级三轴地反力进行了测量、分析和建模,研究结果对蚯蚓运动生物力学、仿蚯蚓移动机器人设计和机器人-环境接触界面调控等具有重要的指导意义.

关键词：移动机器人直线运动运动视频运动生物力学应变片周期性运动环境接触界面调控

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Design and Implementation of Model Predictive Control (MPC) Based Pressure Regulation System for a Precision Agricultural Sprayer

Design and Implementation of Model Predictive Control (MPC) ...

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Khan, Adrees Nasir, Fazal Tufail, Muhammad Haris, Muhammad Khan, Muhammad Tahir Dong, Zhang University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan Jinan China

ISBN: (纸本)9781665464727

Model Predictive Control (MPC) is a control scheme that involves predicting the future behavior of a system and optimizing control actions to accomplish the desired objective. In this study, we develop an intelligent control algorithm, based on MPC to regulate the target pressure in variable-rate agriculture sprayer robots. Modeling and simulation steps of the spraying system are developed using MATLAB/Simulink environment, before passing to the description of the MPC algorithm. Real-time implementation of the MPC algorithm was conducted on an Arduino Mega 2560 controller board by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink to experimentally validate the preliminary results of simulations. The work evaluates MPC to regulate the pressure in the system and compares the results with a traditional PID control system. Moreover, MPC is a novel method for nonlinear system control that achieves zero steady-state error, low transient response, and reduces peak overshoot compared to the results obtained with a PID controller, thereby reducing the waste of chemicals, and minimizing the toxicology and environmental risk. © 2023 IEEE.

关键词： Model predictive control

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Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot

Real-Time Plant Recognition and Crop Row Navigation for Auto...

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Nasir, Fazal Haris, Muhammad Khan, Bilawal Tufail, Muhammad Khan, Muhammad Tahir Dong, Zhang National Center of Robotics and Automation Advanced Robotics and Automation Lab Peshawar Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan Jinan China

ISBN: (纸本)9781665464727

This paper presents a vision-based selective spraying technique for an autonomous agricultural sprayer robot. In traditional methods, excessive chemical spraying cause deleterious effects on human health, environment and becomes uneconomical. In order to reduce the agrochemical wastage encounters in a broadcast spraying, a selective plant spraying method is used for crop chemical treatment. The sensor-based approach assisted with YOLOv7 model is deployed on a custom designed robot for recognizing and localizing the lettuce plants in field. The PID-based pressure controller is designed that minimizes the undesirable fluctuations cause by the opening/closing of solenoid-valve-nozzles (SVNs) during spraying. Thus the nozzle's spraying quality is maintained by keeping the pressure constant to a desired value. A visual servoing scheme for row tracking is presented that uses the detected plant's spatial features. The robustness of the visual-based navigation is validated in the real field experiments. © 2023 IEEE.

关键词： Crops

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SEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

SEMG-Based Gesture Recognition in Multiple Scenarios Using a...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xueze Wang, Minjie Chen, Zihua Wang, Lu Wang, Hongbo Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai & Robotics Ministry of Education Shanghai Engineering Research Center of Ai & Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai & Robotics Institude of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798350386226

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios. © 2024 IEEE.

关键词： Deep learning

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Bi-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Dexterous Manipulations

Bi-VLA: Vision-Language-Action Model-Based System for Bimanu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gbagbe, Koffivi Fidele Cabrera, Miguel Altamirano Alabbas, Ali Alyunes, Oussama Lykov, Artem Tsetserukou, Dzmitry Skolkovo Institute of Science and Technology Intelligent Space Robotics Laboratory Center for Digital Engineering Moscow Russia

ISBN: (纸本)9781665410205

This research introduces the Bi-VLA (Vision-Language-Action) model, a novel system designed for bimanual robotic dexterous manipulation that seamlessly integrates vision for scene understanding, language comprehension for translating human instructions into executable code, and physical action generation. We evaluated the system's functionality through a series of household tasks, including the preparation of a desired salad upon human request. Bi-VLA demonstrates the ability to interpret complex human instructions, perceive and understand the visual context of ingredients, and execute precise bimanual actions to prepare the requested salad. We assessed the system's performance in terms of accuracy, efficiency, and adaptability to different salad recipes and human preferences through a series of experiments. Our results show a 100 % success rate in generating the correct executable code by the Language Module, a 96.06 % success rate in detecting specific ingredients by the Vision Module, and an overall success rate of 83.4 % in correctly executing user-requested tasks. © 2024 IEEE.

关键词： Human robot interaction

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Graph neural network based method for robot path planning

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Biomimetic Intelligence & robotics 2024年第1期4卷 80-87页

作者： Xingrong Diao Wenzheng Chi Jiankun Wang Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical EngineeringSouthern University of Science and TechnologyShenzhen 518055China Robotics and Microsystems Center School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215021China Jiaxing Research Institute Southern University of Science and TechnologyJiaxing 314000China

Sampling-based path planning is widely used in robotics,particularly in high-dimensional state *** the path planning process,collision detection is the most time-consuming ***,we propose a learning-based path planning method that reduces the number of collision *** develop an efficient neural network model based on graph neural *** model outputs weights for each neighbor based on the obstacle,searched path,and random geometric graph,which are used to guide the planner in avoiding *** evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world *** results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.

关键词： Graph Neural Network(GNN) Collision detection Sampling-based path planning

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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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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

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

ISBN: (纸本)9781665464727

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. © 2023 IEEE.

关键词： Antennas

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Quenched PVDF/PMMA Porous Matrix for Triboelectric Energy Harvesting and Sensing

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Energy & Environmental Materials 2025年第1期8卷 229-238页

作者： Assem Mubarak Bayandy Sarsembayev Yerzhigit Serik Abdirakhman Onabek Zhanat Kappassov Zhumabay Bakenov Kazuyoshi Tsuchiya Gulnur Kalimuldina Department of Mechanical and Aerospace Engineering School of Engineering and Digital Sciences Nazarbayev University Institute of Smart Systems and Artificial Intelligence Nazarbayev University Department of Robotics Engineering School of Engineering and Digital Sciences Nazarbayev University Department of Chemical and Materials Engineering School of Engineering and Digital Sciences Nazarbayev University Micro/Nano Technology Center Tokai University

The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their *** introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF) and N,N'-poly(methyl methacrylate)(PMMA) blend with a porous structure via a novel optimized quenching method.

关键词： N,N′-poly(methyl methacrylate) (PMMA) robotic tactile sensors polyvinylidene fluoride (PVDF) quenching triboelectric nanogenerators (TENGs)

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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