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arXiv 2022年

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada The Computer school Hubei University of Arts and Science Xiangyang441000 China The National Yunlin University of Science and Technology Taiwan The Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization. © 2022, CC BY.

关键词： Scheduling

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Development of a robotic teaching interface for human to human skill transfer

Development of a robotic teaching interface for human to hum...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Chenguang Yang Peidong Liang Arash Ajoudani Zhijun Li Antonio Bicchi Zienkiewicz Centre for Computational Engineering Swansea University UK State Key Lab of Robotics Systems Harbin Institute of Technology China Human-Robot Interfaces and Physical Interaction (HRI2) Laboratory of the Istituto Italiano di Tecnologia Genova Italy College of Automation Science and Engineering South China University of Technology Guangzhou China Interdepartmental Research Center "E. Piaggio" Faculty of Engineering University of Pisa Italy

ISBN: (纸本)9781509037636

The tutor-tutee hand-in-hand teaching may be the most effective approach for a tutee to acquire new motor skills. Repetitive nature of such procedures in a group setting usually results in a high labour cost and time inefficiency. Potential solution can be utilizing robotic platforms playing the role of tutors for demonstrating and transferring the required skills. This requires an appropriate guidance scheme to integrate the tutor's motor functionalities into the robot's control architecture. For instance, for hand-in-hand supervision of the writing task, the tutor's corrections can be applied when necessary, while a very compliant motion can be achieved if no errors are detected. Inspired by this behavior, we develop a teaching interface using a dual-arm robotic platform. In our setup, one arm is connected to the tutees arm providing guidance through a variable stiffness control approach, and the other to the tutor to capture the motion and to feedback the tutees performance in a haptic manner. The reference stiffness for the tutors arm stiffness is estimated in real-time and replicated by the tutees robotic arm. Comparative experiments have been carried out on a dual-arm Baxter robot. The results imply that the human tutor is able to intuitively transfer writing skills to the tutee and also show superior learning performance over over some conventional teaching by demonstration techniques.

关键词： Manipulators Robot sensing systems Impedance Training Muscles Manipulator sensing systems Manipulators impedance muscles Functional training Pedagogy Teaching LABOUR COSTS Human compliant motion Writing Skills Arm spiral arm Coaching Motor Skills

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Powered Upper-Limb Control using Passivity-Based Nonlinear Disturbance Observer for Unknown Payload Carrying Applications

Powered Upper-Limb Control using Passivity-Based Nonlinear D...

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IEEE International Conference on robotics and Automation

作者： Min Jun Kim Woongyong Lee Jae Yeon Choi Yong Sik Park Sung Ho Park Goobong Chung Kyung-Lyong Han I1 Seop Choi I1 Hong Suh Youngjin Choi Wan Kyun Chung the Robotics Laboratory School of Mechanical Engineering Pohang University of Science and Technology (POSTECH) Pohang 790-784 Gyung-buk Korea the Korea Institute of Robot and Convergence (KIRO) Pohang 790-784 Gyung-buk Korea the POSCO Technical Research Laboratories Pohang 790-300 Gyung-buk Korea the Department of Electronics and Computer Engineering Hanyang University Seoul Korea the Department of Electronic Systems Engineering Hanyang University Ansan Korea

ISBN: (纸本)9781467380270

This paper proposes a passivity-based nonlinear disturbance observer (DOB) design for a powered upper-limb robot control. The proposed DOB allows for the nonlinearities of the robot dynamics, whereas the typical DOB designs cannot. Moreover, by virtue of the passivity property, human operator and environmental interactions can be embedded in the control loop. As a DOB, the proposed approach has a disturbance observation property that makes the actual robot behave like a nominal model selected by the user. Performance analysis proposes a gain tuning rule. In experimental validation, actual powered upper-limb robot is used to perform unknown payload carrying applications.

关键词： Observer Unknown DOB

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Enhancing Robot Teleoperation in Remote Automation Production through an Event-Triggered Control Strategy

Enhancing Robot Teleoperation in Remote Automation Productio...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Mengke Wang Honghao Lv Ruohan Wang Haiteng Wu Lipeng Chen Yi Chen Haihui Yuan Geng Yang State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Hangzhou China Robotics X Tencent Shenzhen China Intelligent Manufacturing Center DongFang Electric Corporation Academy of Science and Technology Co Chengdu China China South Industries Group Corporation Hangzhou Automation Research Insititute Co. Ltd. China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

With the advancement of intelligent manufacturing, an increasing number of industrial production processes are transforming unmanned operations. This trend is particularly evident in factories that involve potential safety hazards, where the demand for unmanned production is becoming increasingly urgent. Teleoperation, as a core technology for achieving unmanned factories, holds tremendous importance in keeping workers safe from danger. However, traditional teleoperation strategies encounter challenges due to mismatches between master and slave workspaces, making it difficult to achieve both high precision and operation across large workspaces simultaneously. To address this issue, this paper proposes an event-triggered control strategy for a teleoperated robot system. By introducing a foot pedal as the event-triggered switch, the enablement and termination of teleoperation are associated with the activation of the pedal. Through resetting the start position, fine-grained teleoperation is achieved throughout the entire workspace of the robot. To demonstrate the advantages of the proposed control strategy, a mimicked wiring operation task scenario is constructed for experimental verification, and the teleoperation tasks are completed successfully and efficiently.

关键词： Wiring Accuracy Service robots Switches Market research Production facilities Main-secondary

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Programming Mobile Robots in an Educational Context: a Hardware-in-the-loop Approach

Programming Mobile Robots in an Educational Context: a Hardw...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Laiany Brancalião Mariano Alvarez João Coelho Miguel Conde Paulo Costa José Gonçalves Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal University of Léon León Spain Institute of Engineering Systems and Computers Technology and Science (INESC TEC) University of Porto Porto Portugal Laboratory for Sustainability and Technology in Mountain Regions (SusTEC) Instituto Politécnico de Bragança Bragança Portugal

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In this paper it is presented a Hardware-in-the-loop (HIL) mobile robot programming approach, to be applied in a robotics educational context. The motivation to apply this approach is the fact that students can program the robots without access to the robot hardware, but still maintain some important closed loop control critical features, such as a realistic lag time and the possibility for a larger number of students to program at the same time. Therefore, the developed software is applied to the real hardware without any change. The HIL approach was applied to provide a simulation close to reality, once the processing occurs in the real robot processor and the actuation and sensorization inside the simulation, adding to the advantage to test the firmware avoiding damage in the physical robot.

关键词： Context Robot sensing systems Hardware Software Mobile robots Information technology Programming profession Microprogramming

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Distributed Optimization by Network Flows with Spatio-Temporal Compression

arXiv

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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation

arXiv

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arXiv 2022年

作者： Goel, Yash Vaskevicius, Narunas Palmieri, Luigi Chebrolu, Nived Stachniss, Cyrill The laboratory for Photogrammetry and Robotics University of Bonn Germany Robert Bosch GmbH Germany Robert Bosch GmbH Corporate Research Stuttgart Germany The Dynamic Robot Systems Group University of Oxford United Kingdom The University of Bonn Germany The Department of Engineering Science the University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

We investigate the task of object goal navigation in unknown environments where the target is specified by a semantic label (e.g. find a couch). Such a navigation task is especially challenging as it requires understanding of semantic context in diverse settings. Most of the prior work tackles this problem under the assumption of a discrete action policy whereas we present an approach with continuous control which brings it closer to real world applications. We propose a deep neural network architecture and loss function to predict dense cost maps that implicitly contain semantic context and guide the robot towards the semantic goal. We also present a novel way of fusing mid-level visual representations in our architecture to provide additional semantic cues for cost map prediction. The estimated cost maps are then used by a sampling-based model predictive controller (MPC) for generating continuous robot actions. The preliminary experiments suggest that the cost maps generated by our network are suitable for the MPC and can guide the agent to the semantic goal more efficiently than a baseline approach. The results also indicate the importance of mid-level representations for navigation by improving the success rate by 7 percentage points. Copyright © 2022, The Authors. All rights reserved.

关键词： Navigation

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Prototyping and Control of an Educational Manipulator Robot

Prototyping and Control of an Educational Manipulator Robot

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： João Coelho Laiany Brancalião Mariano Alvarez Paulo Costa José Gonçalves Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal University of Léon León Spain Institute of Engineering Systems and Computers Technology and Science (INESC TEC) University of Porto Porto Portugal Laboratory for Sustainability and Technology in Mountain Regions (SusTEC) Instituto Politécnico de Bragança Bragança Portugal

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This article presents the prototyping of an educational manipulator robot, based on the "EEZYbotARM Mk2" robot, tailored for first-year master’s students in the field of robotics. The project encompasses the assembly of the robot arm, computation of both forward and inverse kinematics, and analysis of two path-planning movement algorithms. These features are consolidated into an Arduino library to streamline the process for students to generate instructions for the robot. The "EEZYbotARM Mk2" features a three-degree-of-freedom revolute arm with a gripper that remains parallel to its base at all times, enhancing its suitability for educational applications such as pick-and-place tasks. The article provides detailed descriptions of the materials and methods employed, along with proposed challenges for student engagement.

关键词： Kinematics Manipulators Libraries Information technology Grippers Assembly

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Magnetic-Actuated Flexible Instruments with Enhanced Bending Capability through Magnetic Distribution Optimization

Magnetic-Actuated Flexible Instruments with Enhanced Bending...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Yuanrui Huang Runyu Ma Jian Hu Mingcong Chen Jian Chen Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of General Engineering Beihang University Beijing China Centre of AI and Robotics (CAIR) Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

Magnetically-actuated flexible surgical robot has promising applications for ultra-narrow and tortuous orifices. However, maximizing its bending capability and manipulability remains a significant hurdle for clinical use. In this study, we introduce an objective function that can accurately predict a catheter's bending angle with given numbers of internal permanent magnets (IPMs) under various magnetic fields through iterative calculation. The optimal magnets distribution that yields the maximal controllable bending angle, could be determined by optimizing this objective function. The effectiveness of this method is validated through simulations and experiments. Additionally, we propose a simple control scheme to control the bending angle of the magnetically-actuated catheter based on the piecewise constant curvature model. The experimental results demonstrate that the proposed method significantly improves the motion accuracy of the catheter. The catheter with the optimized magnet distribution achieved superior tracking performance with an Integral Absolute Error (IAE) of 1.08 rad·s and maximal controllable bending angle of 150°.

关键词：

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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