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DG-NBV: A Cognitive Framework for Direct Generation of Next Best View in Continuous View Space

IEEE Transactions on Cognitive and Developmental systems 2024年

作者： Liu, Zhicheng Cao, Zhiqiang Li, Jianjie Guan, Peiyu Yu, Junzhi Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Peking University Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

The next best view (NBV) plays an important role in the three-dimensional object reconstruction. Existing NBV methods mainly adopt the generate-and-test strategy to select the best view in the discrete view space. This affects the adaptation to diverse objects. To solve this problem, a new NBV paradigm to directly generate the next best view in the continuous view space according to the input point cloud is proposed. Specifically, a point cloud feature extraction module with learnable view and position tokens is presented. These tokens are added upon the neighborhood and the position features of the point cloud to fully mine global contextual information, enhancing the feature representation. The predicted view from the proposed network is linked to a pre-trained view evaluation network. By duplicating this prediction view and then concatenating the duplication result with the extracted point cloud feature, the evaluation network is endowed with the ability to evaluate arbitrary views. Take the evaluation score of the evaluation network corresponding to the predicted view as the supervision signal, the network is trained. In this way, an effective solution of the next best view selection in the continuous view space is obtained. Accordingly, the adaptability to different objects is reinforced. Experimental results on the ShapeNet and MIT CSAIL datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Feature extraction

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A Vision-Based Two-Stage Fruit Outer Diameter Detection Method

A Vision-Based Two-Stage Fruit Outer Diameter Detection Meth...

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IEEE International Conference on robotics and Biomimetics

作者： Yifei Hao Shuaizheng Yan Zhengxing Wu Bingwei He Chenghua Li Junzhi Yu Department of Mechanical Engineering Fuzhou University Fuzhou China Laboratory of Cognitive and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Guangzhou Shenghui Cleaning Service Co. Ltd. Guangzhou China Department of Advanced Manufacturing and Robotics State Key Laboratory for Turbulence and Complex Systems BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Regarding the mobile monitoring system in agricultural automation, the outer diameter monitoring of greenhouse vegetables has significant implications for accurately regulating fruit growth and cultivation plans. In this paper, a novel vision-based two-stage outer diameter detection method is proposed, including the segmentation and outer diameter calculation of greenhouse tomato images. To address the issues of multiple tomatoes in a single image and the similarity in color between tomatoes and the background, a YOLOv8 network embedded with convolutional block attention module is constructed for detection and segmentation in the first stage. Additionally, to solve the problem of inaccurate outer diameter detection caused by different tomato poses and pedicles, a defined central axis method is proposed, which can adaptively filter out the maximum outer diameter candidates and calculate accurate values utilizing depth information in the world coordinate system. The backbone network is fine-tuned on the pre-trained YOLOv8 model with 1000 tomato images. Extensive measurement experiments, involving 215 tomatoes in 80 images, is conducted to validate the effectiveness of our proposed approach. Compared to the ground truth, we obtain a measurement error of 4.27 mm, the coefficient of determination value R2 is 0.9349.

关键词： Image segmentation Measurement errors Adaptation models automation Image color analysis Robot kinematics Biomimetics Adaptive filters Information filters Monitoring

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Admittance Control based Hemiplegic Exoskeleton Motion Control

Admittance Control based Hemiplegic Exoskeleton Motion Contr...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： He, Bailin Zhou, Xin Huai, Yaping Liu, Jiaqing Li, Pengbo Wang, Can Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen518000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

In this paper, a method based on admittance control is developed to improve patients' comfort wearing an exoskeleton robot for rehabilitation training. In our previous work, we designed a modular and convenient hemiplegic exoskeleton robot. The mechanical structure will cause stiffness when it comes to contact with the human body in practical applications. We introduced admittance control to improve the flexibility of the exoskeleton robot and improve the subjects' comfort. This paper considers coupling of model between the exoskeleton and the human body as the human-computer interaction force of the system at the main human-computer interaction point. Measuring the interaction force between exoskeleton members and limbs, the work uses the virtual admittance model to correct the system's inherent characteristics and studies the control method's stability with Lyapunov's theory. The experiment collected the subject movement information which was converted the movement information of the exoskeleton. Human-computer interaction forces in the thigh and calf were measured while the exoskeleton assisted the subject's movement. The experimental results show that adding the admittance controller reduces the human-computer interaction force from 5g to 2g and 6g to 3g, respectively. © 2022 IEEE.

关键词： Human computer interaction

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Variation of sEMG with Muscle Fatigue for Lower Limb Stroke Patient During Walking

Variation of sEMG with Muscle Fatigue for Lower Limb Stroke ...

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IEEE International Conference on robotics and Biomimetics

作者： Wenze Shang Ruisen Huang Wei-Hsin Liao Xinyu Wu Fei Gao Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

sEMG signal is widely used and explored in control strategies of powered assistive human-robot interaction systems due to its non-invasive nature and ability to estimate motion intention well. However, prolonged use of the sEMG-based systems leads to muscle fatigue, contributing to unstable sEMG signals and regarding the performance of sEMG-based control strategies. In this study, three subjects, including two healthy subjects and one stroke subject, participated in our experiment, and the time domain, frequency domain, and complex features extracted from their $sEMG$ signals were utilized to evaluate muscle fatigue. The results indicate that $sEMG$ from the stroke side of the stroke subject contains more non-linear and chaotic parts, and the statistical differences between different trials are more significant, indicating that stroke has a more significant impact on the stroke side of the stroke subject. This study suggests that RMS in the stroke side of stroke subjects is less than that in others, whereas FuzzyEn in the stroke side of stroke subjects is greater than that in others, indicating that stroke leads to different motion patterns and more susceptibility to muscle fatigue.

关键词： Legged locomotion Limbs Frequency-domain analysis Biomimetics Human-robot interaction Muscles Fatigue Feature extraction Time-domain analysis Robots

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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Improve the Performance of Active Knee Prosthesis During Stairs Ascent by Using the Leading Reference Control System

Improve the Performance of Active Knee Prosthesis During Sta...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Qinpeng Huang Wenhao Wei Bojun Huang Hui Wang Pingao Huang Guanglin Li School of Electronic Engineering and Automation Guilin Key Laboratory of Intelligence Integrated Automation in Guangxi Universities Guilin University of Electronic Technology Guangxi China CAS Key Laboratory of HumanMachine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) and the SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Strong coupling between the human and machine exists in many cyborg and bionic systems (CBS). Some input signals for control system barely change after the outputs work. In this situation, it is hard for the controller to calculate the required reference output of system. For example, in the stance phase of stairs ascent gait using an active knee prosthesis (AKP), most AKPs use the Inertial Measurement Unit (IMU) signal and the Ground Reaction Forces (GRF) signal to control the output of knee joint torque. However, these two signals hardly change unless the controlled object (joint torque) is already close to the actual requirement. Therefore, this study introduces a leading reference control system (LRCS) that uses an overshoot reference value to make the AKP more effective in providing positive energy in the abovementioned situation. In addition, the LRCS with different parameters was evaluated through the experiments of stairs ascent with AKP. Results show that LRCS with the suitable parameter can provide greater torque output when going upstairs, making the subject go upstairs easier.

关键词： Knee Couplings Torque Measurement units Inertial navigation Stairs Control systems

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Modeling Neural Representation of Social Recognition Information with Recurrent Neural Networks 17

Modeling Neural Representation of Social Recognition Informa...

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17th International Conference on Complex Medical Engineering, CME 2023

作者： Song, Fei Wang, Xinnian Zhan, Yang Tang, Fengzhen Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of Life Sciences Division of Life Science and Medicine University of Science and Technology of China Hefei230026 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Systems Science Beijing Normal University Beijing100091 China Chinese Institute for Brain Research Beijing102206 China

ISBN: (纸本)9798350316117

Social recognition, the ability to differentiate between familiar and unfamiliar individuals, is crucial for animals' survival and reproduction. Neuroscience research has predominantly focused on investigating the brain regions and circuits that regulate social recognition. However, a lingering question pertains to the precise mechanisms underlying the social recognition process. To address this question, we employed a recurrent neural network (RNN) to determine whether an agent can recognize familiar individuals without involving higher-level decision-making processes. This approach takes into consideration that animal behavioral performance may not directly mirror complex cognitive functions. The RNN was trained using the full-FORCE learning paradigm. Our simulation results revealed three distinct subsets of neurons with specific functions: one dedicated to representing novel individuals and two others representing the same social individual in both unfamiliar and familiar states. We propose a hypothesis regarding the potential memory computing processes involved in these operations. © 2023 IEEE.

关键词： Animals

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Region-Aware Grasping for Stacked Workpieces: A 6D-Wise Label Self-Generation Method and Robust Evaluation Strategy

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IEEE Transactions on automation Science and Engineering 2025年 22卷 16899-16912页

作者： Xungao Zhong Tao Gong Junzhi Yu Jiaguo Luo Chengxian Zhou Xunyu Zhong Qiang Liu School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China School of Aerospace Engineering Xiamen University Xiamen China School of Engineering Mathematics and Technology University of Bristol Bristol U.K. Department of Psychiatry University of Oxford Oxford U.K.

The high-quality datasets and generalized network model combined with robust evaluation strategies serve as a significant benchmark for developing new policies for industrial bin-picking. In this paper, we propose the concept of region-aware grasping, a sim2real cutting-edge system to generate and evaluate 6D poses for robots to pick up novel workpieces from stacked environments. It consists of Region-Aware-Dataset, a large-scale synthetic point cloud grasp dataset; and Semantic-Graspnet, a 6D-wise affordance policy that predicts full 6D grasp pose for stacked workpieces. The introduced Semantic-Graspnet transforms the 6D pose prediction problem into semantic categorization via point cloud encoding and decoding. Meanwhile, we propose a robust evaluation strategy based on pose evaluation and mechanical grasping evaluation, which enhances the robot’s grasping success rate and sorting efficiency. In real industrial tasks, the robot achieves a grasp completion rate of 91.3% in cluttered scenes and 89.2% in densely stacked scenes, demonstrating state-of-the-art results in robotic picking-and-placing applications. Note to Practitioners—This work is inspired by the robotic universal AI technique, which holds an indispensable position in industrial as bin-picking tasks. Leveraging this technology, the robot can proficiently perform picking-and-placing for diverse workpieces. The prior robot grasping policies primarily centered on maximizing the optimization of grasp detection to ensure successful grasping. Nevertheless, limitations still exist regarding efficient datasets, generalized network models, and robust evaluation strategies. Therefore, in order to effectively curtail the development cycle of robot picking-and-placing applications, this paper puts forward a novel region-aware grasping policy for stacked workpieces. We have investigated an autonomous grasp label annotation method and constructed a large-scale synthetic point cloud grasp dataset. Subsequently, a seman

关键词： Grasping Point cloud compression Training Service robots Affordances Collision avoidance Semantics Predictive models Electronic mail Deep learning

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Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Huijie Dong Chunyang Zhao Tiezhu Qiao Jian Wang Di Chen Yan Meng Junzhi Yu Ministry of Education Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Taiyuan China Institute of Automation Laboratory of Cognitive and Decision Intelligence for Complex System Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named ‘CourierFish’. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks.

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NeRF in robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in robotics and The Advance of NeRF in robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： robotics

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