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2024 China Automation Congress, CAC 2024

作者： Xiong, Yan Zhang, Pengfei Chen, Di Yu, Junzhi State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China Shandong Nuclear Power Company LTD Yantai265116 China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9798350368604

Water-air trans-media navigators have multi-media motion modes, breaking through the limitation that traditional robots can only operate in a single medium. Autonomous navigation is an indispensable component of the process of trans-media motion. Most of the current work has only completed the development and testing of prototypes, and the problem of task-oriented motion planning and control needs to be solved. In this paper, a safe and efficient hierarchical approach is proposed. First, a novel rapidly exploring random tree-based algorithm is proposed to search for a suitable path. Then, by fixing bound waypoints, the time-optimization trajectory is planned. Finally, the trajectory tracker is designed based on nonlinear model predictive control (NMPC). Simulation experiments numerically validate the proposed method, offering some new insights for further trans-media motions. © 2024 IEEE.

关键词： Trajectories

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

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Medical Technologies National Conference (TIPTEKNO)

作者： Ayse Gurluk Hafiz Muhammad Bilal Elif Hocaoglu Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Türkiye Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Türkiye Department of Electrical and Electronics Engineering Living Robotics Laboratory Istanbul Medipol University Istanbul Türkiye

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

This study presents a more accurate evaluation method for assessing ankle spasticity compared to conventional goniometric techniques by utilizing measurements obtained from Inertial Measurement Units (IMU) and surface electromyography (sEMG) devices. Integrating IMU and EMG sensors provides substantial benefits for accurately determining the range of motion (ROM) associated with spasticity during targeted movements. In this study, the proposed method provides precise measurement and an objective evaluation of onset time detection of antagonistic muscle co-contraction, and accordingly the ROM of the actuated joint. Moreover, the implementation of cost-effective wearable sensors enables the monitoring of muscle spasticity in clinical environments and can be particularly valuable in regions with limited access to experienced therapists, providing a more efficient approach to tracking spasticity levels.

关键词： Ankle Measurement units Inertial navigation Muscles Electromyography Sensors Reliability Motion control Read only memory Monitoring

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Learn to Coordinate: A Whole-Body Learning from Demonstration Framework for Differential Drive Mobile Manipulators

Learn to Coordinate: A Whole-Body Learning from Demonstratio...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Yang, Yuqiang Huang, Darong Chen, Chen Zeng, Chao He, Yanong Yang, Chenguang South China University of Technology Automation Science and Engineering Guangzhou China Application Innovate Laboratory Huawei Technologies Co. Ltd Shenzhen China Universitat Hamburg Technical Aspects of Multimodal Systems Hamburg Germany Bristol Robotics Laboratory University of the West of England Bristol United Kingdom

ISBN: (纸本)9798350337020

This paper proposes a whole-body learning from demonstration (LfD) framework that enables differential drive mobile manipulators to learn coordination working and disturbance rejection. First, an efficient kinesthetic teaching method is devised based on the weighted least-norm (WLN) inverse kinematics solution and an admittance controller, which facilitates human users to guide the mobile manipulator to perform tasks. Second, we propose a whole-body LfD framework through Gaussian Process, which endows the mobile manipulator's skill learning process with features of large-scale convergence, coordination working and disturbance rejection, after just a few human demonstrations. The proposed learning framework also allows for human-in-the-loop correction when the whole-body is conducting a task. Finally, the effectiveness of the proposed framework is verified via two simulations and a pick-and-place experiment. Supplementary video for this paper is available in github https://***/yuqiang-yang/SMC2023-Video. © 2023 IEEE.

关键词： Gaussian Process Learning from Demonstration Wholebody Control

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Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat 12

Learning Rat-Like Behavioral Interaction Using a Small-Scale...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Gao, Zihang Jia, Guanglu Xie, Hongzhao Guo, Xiaowen Fukuda, Toshio Shi, Qing Beijing Institute of Technology Ministry of Education Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Key Laboratory of Biomimetic Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In the existing robot-rat interaction, robots usually exert influence as stimuli to observe the response of target rats. However, the above single-stimulation model from robot to rat lacks a two-way communication. Therefore, we proposed a method to learn the rat-like behavioral interaction. First, we constructed two behavior patterns in the interaction process between the two rats: the individual pattern and the interaction pattern. We divided the roles of the two rats into active stimulator and passive receiver. The stimulator executes the individual pattern, and the receiver switches between the two patterns. Which pattern the receiver executes depends on the probability of interaction. Secondly, we proposed a hypothesis concerning the behavioral interaction between two rats and controlled the interaction process between two robots. Information entropy was used to measure the similarity between two robots and two rats. The results proved that the probability of interaction between rats depends on the distance of centroid and average relative velocity, and we obtained the optimal solution to express the relationship between them. In the future, the improvement of robots' interaction ability with rats will highly benefit from this study. © 2022 IEEE.

关键词： Rats

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A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

arXiv

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arXiv 2023年

作者： Bilić, Ivan Marić, Filip Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

Autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot’s kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results. © 2023, CC BY-NC-ND.

关键词： Manipulators

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Design and Validation of a Wireless Drone Docking Station

Design and Validation of a Wireless Drone Docking Station

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Dario Stuhne Goran Vasiljević Stjepan Bogdan Zdenko Kovačić LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

Drones are increasingly operating autonomously, and the need for extending drone power autonomy is rapidly increasing. One of the most promising solutions to extend drone power autonomy is the use of docking stations to support both landing and recharging of the drone. To this end, we introduce a novel wireless drone docking station with three commercial wireless charging modules. We have developed two independent units, both in mechanical and electrical aspects: the energy transmitting unit and the energy receiving unit. We have also studied the efficiency of wireless power transfer and demonstrated the advantages of connecting three receiver modules connected in series and parallel. We have achieved maximum output power of 96.5 W with a power transfer efficiency of 56.6% for the series connection of coils. Finally, we implemented the system in practice on a drone and tested both energy transfer and landing.

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Serial Chain Hinge Support for Soft, Robust and Effective Grasp

Serial Chain Hinge Support for Soft, Robust and Effective Gr...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Dario Stuhne Jelena Vuletić Marsela Car Matko Orsag LARICS Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper presents a serial chain hinge support, a rigid yet flexible structure that improves the mechanical performance and robustness of soft-fingered grippers. Gravity can reduce the integrity of soft fingers in horizontal approach, resulting in lower maximum payload caused by a large deflection of fingers. To substantiate our claim we performed multiple experiments on the payload and deflection of the SofIA gripper under both horizontal and vertical approaches. In addition, we show that this reinforcement does not impede the original compliant behavior of the gripper, maintaining the original kinematic model functionality. Also, we showcase the proprioceptive and exteroceptive capabilities for two opposing manipulation problems: grasping small and large objects. Finally, we validated the improved SofIA gripper in agricultural and everyday activities.

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A Novel Feature Learning-based Bio-inspired Neural Network for Real-time Collision-free Rescue of Multi-Robot systems

arXiv

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arXiv 2024年

作者： Li, Junfei Yang, Simon X. Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this paper, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations. © 2024, CC BY.

关键词： Multipurpose robots

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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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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Hao, Yifei Yan, Shuaizheng Wu, Zhengxing He, Bingwei Li, Chenghua Yu, Junzhi Fuzhou University Department of Mechanical Engineering Fuzhou350108 China Institute of Automation Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Guangzhou Shenghui Cleaning Service Co. Ltd. Guangzhou511436 China BIC-ESAT College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

ISBN: (纸本)9781665481090

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. © 2024 IEEE.

关键词： Fruits

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Human Localization in Warehouse Environments based on a Wearable Camera Sensor Suite and Dynamic Ultra-Wide Band Nodes 20

Human Localization in Warehouse Environments based on a Wear...

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20th International Conference on Advanced robotics, ICAR 2021

作者： Popovic, Goran Cviic, Igor Markovic, Ivan Petrovic, Ivan Laboratory For Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9781665436847

Modern warehouses, equipped with an autonomous robots fleet, suffer from an efficiency deficit during human interventions in the shop floor area, since, during such interventions, all the robots must stop for safety reasons. This particularly affects large warehouses which could extremely benefit from a solution that would allow human-robot collaboration during such interventions. This solution should only stop the robots that are in the vicinity of the human and reroute the others, and it requires the following prerequisites: (i) reliable relative ranging of robots with respect to human workers that would ensure safety, and (ii) reliable localization of human workers in the warehouse. In this paper we propose a system for reliable human localization that is based on fusing cues from measured relative ranges with respect to moving robots and a camera system that is worn by the human worker. Since global locations of the robots in the warehouse are known, we use the relative ranges to robots in an indoor GPS-like fashion to provide global human location updates, while the wearable cameras provide visual odometry, i.e., the relative motion of the human, and another global cue by detecting ground fiducial markers used by robots. The fusion is performed within a graph-based optimization framework that also includes a visual odometry error model. We carried out an experimental analysis on three different datasets to validate the performance of the proposed approach and compared it to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results demonstrate that our approach yields reliable human location within warehouse like environments. © 2021 IEEE.

关键词： Warehouses

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