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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Li, Yuxiao Wang, Can Su, Zhilong Duan, Shengcai Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

The application of High-Definition map can realize centimeter-level position in urban scenes, the development of deep learning has made great breakthrough in the point cloud dynamic obstacle recognition. All these technologies make obstacle detection and tracking based on High-Definition map effectively realize in the modern smart city scene. It is different from the previous obstacle detection and tracking methods based purely on vision the using of High-Definition map can provide high-precision positioning and reduce the difficulty of point cloud classification. The using of lidar also solves the problem of up-to-scale in dynamic detection. In this paper, we put forward a new Multi-camera Lidar Point Cloud Map, we complete the map at normal speed on the highway and get a satisfactory result. At the same time, we also find a robust combination of traditional kalman filter, Hungary algorithm and current deep learning to solve dynamic obstacle tracking and detection. The experimental results show that our system can effectively complete the special problem of generating map and target tracking on urban scene. © 2019 IEEE.

关键词： Optical radar

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Correction to: Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project

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International Journal of Social robotics 2022年第9期14卷 2045-2045页

作者： Luperto, Matteo Monroy, Javier Renoux, Jennifer Lunardini, Francesca Basilico, Nicola Bulgheroni, Maria Cangelosi, Angelo Cesari, Matteo Cid, Manuel Ianes, Aladar Gonzalez-Jimenez, Javier Kounoudes, Anastasis Mari, David Prisacariu, Victor Savanovic, Arso Ferrante, Simona Borghese, N. Alberto Applied Intelligent System Lab Department of Computer Science University of Milan Milano Italy Machine Perception and Intelligent Robotics Group (MAPIR) Department of System Engineering and Automation University of Malaga and Instituto de Investigación Biomédica de Málaga–IBIMA Malaga Spain Machine Perception and Interaction Lab Örebro University Örebro Sweden NearLab Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy AB.Acus SRL Milan Italy University of Manchester Manchester UK IRCCS Istituti Clinici Scientifici Maugeri University of Milan Milan Italy Junta de Extremadura Merida Spain Korian Milan Italy SignalGeneriX Limassol Cyprus EURECAT Barcelona Spain University of Oxford Oxford UK Smart Com Ljubjana Slovenia

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An Image segmentation method by multi-scale local thresholding based on class uncertainty theory

An Image segmentation method by multi-scale local thresholdi...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhou, Likai Jiang, Yuyang Liang, Guoyuan Wu, Xinyu Zhu, Jiafeng Xiang, Huaikun Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences China School of Automotive and Transportation Engineering Shenzhen Polytechnic Shenzhen Guangdong Province China School of Math Sun Yat-sen University Guangzhou Guangdong Province China

ISBN: (纸本)9781728163215

Image segmentation is one of the most important core technologies in the field of image processing and computer vision, which has lots of applications specifically in medical image analysis. Due to medical imaging mechanism, medical images usually suffer from heavy noises, uneven intensity distribution and fuzzy boundaries between biological tissues. That makes classical segmentation methods based on global or local thresholding difficult to get accurate results. This paper proposes an image segmentation method by multi-scale local thresholding based on class uncertainty theory. Firstly, the original image is divided into a set of sub-regions with different scales by using a multi-layer pyramid structure. Secondly, the energy function with inequality constraints is constructed based on class uncertainty and region uniformity. Then by an iterative process, the optimal local threshold for each sub-region are calculated by an optimization algorithm for each layer until the final optimal threshold mask is determined for image segmentation. Experimental results verify that the proposed method can effectively eliminate the interferences caused by image noises, intensity unevenness and fuzzy boundaries, and preserve details of object structure while segmenting the object from the background. Segmentation results demonstrate the superior performance of the proposed method by comparison with supervised range-constrained thresholding methods (RCOtsu), classical global/local Otsu method, and the minimization of homogeneity- and uncertainty-based energy method (MHUE). © 2019 IEEE.

关键词： Image segmentation

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Parameter Tuning of PID Controller of Servo system Based on Particle Swarm Optimization Algorithm

Parameter Tuning of PID Controller of Servo System Based on ...

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作者： Chen Lianbo Xiong Xinhong Zheng Yousheng Zhang Yanhui Zhang Guoqing Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology School of Logistics Engineering Wuhan University of Technology

In order to improve the control precision of permanent magnet synchronous motor servo drive, a Proportional-Integral-Differential(PID) control method based on particle swarm optimization(PSO) algorithm is proposed. Firstly, the mathematical model of the permanent magnet synchronous motor was established. Then the PSO algorithm was used to adjust the parameters of the PID controller. Finally, the simulation experiment was carried out under the MATlab/Simulink environment to test its performance. Compared with the traditional manual parameters tuning, the PID controller based on PSO algorithm had better performance and better stability.

关键词： swarm Parameter Tuning of PID Controller of Servo system Based on Particle Swarm Optimization Algorithm Swarm

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Research on Position Sensorless Control of PMSM Based on Improved SMO

Research on Position Sensorless Control of PMSM Based on Imp...

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作者： Zhang Guoqing Xiong Xinhong Zheng Yousheng Zhang Yanhui Chen Lianbo Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology School of Logistics Engineering Wuhan University of Technology

The sliding mode observer（SMO） can effectively achieve the permanent magnet synchronous motor（PMSM） to perform position sensorless control, but there are still drawbacks of system chattering and high complexity. In this paper, an improved SMO is established based on hyperbolic tangent function and Kalman filter. The system chattering is effectively suppressed, and the accuracy of position and speed observation error is significantly improved without phase compensation. It is found that the convergence rate of the hyperbolic tangent function has a strong correlation with the system chattering degree and the speed observation error. When a is between 3 and 4, the speed error accuracy of the improved SMO is the optimum situation.

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Automatic Cell Assembly by Two-fingered Microhand

Automatic Cell Assembly by Two-fingered Microhand

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2019 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Junnan Chen Xiaoming Liu Shengnan Dong Pengyun Li Xiaoqing Tang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Intelligent Robotics Institute School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing CHINA Department of Systems Innovation Graduate school of Engineering Science Osaka University Toyonaka Osaka Japan Global Alliance Lab The University of Electro-Communications Tokyo Japan

We have successfully achieved manipulation and assembly of microbeads having the size of 100μm diameter by hemispherical end-effectors with high stability and accuracy. The motivation of achieving assembly of actual cells lies in the great significance of it in tissue regeneration and cell analysis. Firstly, the most difficult problem we need to solve is the releasing problem caused by adhesion force. The viscosity on cell surface is much larger than the microbeads which makes cell releasing challenging. Secondly, the cell can generate its deformation, then contact area with end-effector will change during grasping process. This may influence the adhesion force and also bring problem to releasing. Thirdly, cell is much smaller, around 15μm in diameter, so we need to fabricate smaller end-effector to achieve successful manipulation and ensure the stability in the meantime. In this paper, we realize the manipulation by decreasing the adhesion forces and apply vibration to release a cell stably. We found the appropriate scale size for the end-effector is around 10μm diameter. It can not only grasp a 15μm cell but also bring little interference to the environment. As a demonstration of the proposed manipulation method, the repeated experiments were conducted to explore the dependence of adhesion force on the grasping distance, which can be helpful in the improvement of successful rate. Finally, we achieved automatic cell assembly using Hela cells.

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A lightweight soft exoskeleton in lower limb assistance

A lightweight soft exoskeleton in lower limb assistance

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Chinese Automation Congress (CAC)

作者： Yu Zhang Zhuo Wang Chunjie Chen Tao Fang Ruimei Sun Yanjie Li School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China BeiJing Aerospace Wanyuan Science Technology Co. Ltd. Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The soft exoskeleton is a wearable robot which is functioned to improve the capacity of human walking or loading. The metabolic of human is increase with the additional load, one integrated design method is used to reduce this effect in this paper. Meanwhile, in order to enhance the convenience and decrease the disturbance to wearer, the wireless module is used to replace the signal line. The nonlinear characteristics and uncertainty of flexible material make the control of soft exoskeleton system be a challenge, a switching controller is proposed to deal with this problem in this paper. It consists of Iterative learning control (ILC) based stable controller and admittance control based unstable controller, functioned with wearers walking stable and not. An experiment of force tracking is conducted to verify the controller performance in this paper, and the result demonstrate that the tracking is well after 20 iteration. In order to evaluate the performance of assistance of soft exoskeleton, five health adult male take part in metabolic experiment By compare the metabolic rate between the wear soft exoskeleton and not wear, the metabolic rate are decrease 7.33%, 14.56% and 10.45% with the walking speed of 3km/h, 5km/h and 7km/h, which are corresponding absolute values decrease of 0.46W/kg, 1.60W/kg and 1.21W/kg respectively.

关键词： AC motors Mechanical cables Wireless communication Pulleys Microprocessors Micromechanical devices Germanium

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Gait Phase Classification Based on sEMG Signals Using Long Short-Term Memory for Lower Limb Exoskeleton Robot

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IOP Conference Series: Materials Science and engineering 2020年第1期853卷

作者： Ye Yuan Ziming Guo Can Wang Shengcai Duan Lufeng Zhang Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University.

In this work, we present a Long Short-Term Memory Model (LSTMM) for gait phase classification based on sEMG signals to control the lower limb exoskeleton robot which can recognize 2 phases (Stand and Swing) of leg phases between the foot and ground. This model only needs four sEMG signals to control the lower limb exoskeleton robot helping the hemiplegia patient walking. Compared with the existing methods, the proposed model not only avoids the complex sensor systems but also enhances the accuracy of gait phase classification. The experimental results first verify the availability of sEMG data acquisition system on the lower limb exoskeleton robot made by the Shenzhen Institutes of Advanced Technologies (SIAT) by quantify the system with gold standard optoelectronic system Vicon, then show that the proposed LSTMM is significantly higher on prediction accuracy and has better robustness for gait phase classification to control the lower limb exoskeleton robot with different speeds. Finally, the maximum accuracy of LSTMM on the gait phase classification is 97.89%.

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ROS-health: An open-source framework for neurorobotics

ROS-health: An open-source framework for neurorobotics

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2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2018

作者： Beraldo, Glori Castaman, Nicola Bortoletto, Roberto Pagello, Enrico Del, Millan Jose R. Tonin, Luca Menegatti, Emanuele Intelligent Autonomous System Lab Department of Information Engineering University of Padova Padua Italy Italy IT+ROBOTICS SRL Contrá Valmerlara 21 Vicenza Italy Defitech Depart in Brain-Machine Interface École Polytechnique Fédérale de Lausanne Chemin des Mines 9 GenevaCH-1202 Switzerland

ISBN: (纸本)9781538659748

This paper aims at providing a preliminary description of ROS-Health, a novel framework for neurorobotics based on the middleware Robot Operating system (ROS). The increased interest in the neurorobotics field and the proliferation of several (neuro)physiological-based applications to control robotics devices made clear the importance to establish a standardized research platform in order to facilitate the distribution of the software, the replication of experimental results and the creation of an unified community to share and manage the code in the years. For this reason, we propose a common platform developed in the ROS ecosystem that takes advantage of its tools and capabilities. Furthermore, we describe the design guidelines that we are following in the preliminary definition of ROS-Health architecture. Finally, we present two illustrative use cases that highlight the advantages and benefits of the adoption of ROS-Health. © 2018 IEEE.

关键词： Robot Operating system

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A holistic visual place recognition approach using lightweight CNNs for significant viewpoint and appearance changes

arXiv

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arXiv 2018年

作者： Khaliq, Ahmad Ehsan, Shoaib Chen, Zetao Milford, Michael McDonald-Maier, Klaus Embedded and Intelligent System Lab School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Vision for Robotics Lab Eth Zurich Zurich8092 Switzerland Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Australia

This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot applications

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