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

作者： Chen, Wei Wu, Jiahao Zhou, Jianshu Wang, Hesheng Wang, Yudong Cheung, Tak Hong Liu, Yun-Hui Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China International Peace Maternity and Child Health Hospital Shanghai Jiaotong University School of Medicine Department of Obstetrics and Gynecology Shanghai China Prince of Wales Hospital Chinese University of Hong Kong Department of Obstetrics and Gynaecology Hong Kong

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Multi-link serial robots have gained growing popularity in robot-Assisted surgeries with the advantages of flexibility and versatility. This type of robot has be introduced into the uterus manipulation in laparoscopic hysterectomy (UMLH) to mitigate the disadvantages of our previous robotic uterus manipulator. Compared with the other surgical tasks (e.g. laparoscope manipulation, orthopedic and neurosurgery surgeries), the UMLH has a more narrow workspace and a stringent task requirement. Selecting the proper initial configuration of the robot is a key issue for such kind of surgical operations. In this paper, we develop a scheme to select appropriate initial configurations for the UMLH. Moreover, our method can satisfy both the static RCM and dynamic RCM. In an dynamic RCM, the task path or trajectory depends on the robot initial pose and arbitrary (or indefinite) with respect to the robot base frame. We also proposed a criterion, i.e. configuration-Type task capability (CTTC), to measure the task capability of a given configuration-Type for the dynamic RCMs. Simulations are carried out to validate our scheme. © 2022 IEEE.

关键词： Neurosurgery

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A Multi-model Fusion of LiDAR-inertial Odometry via Localization and Mapping

A Multi-model Fusion of LiDAR-inertial Odometry via Localiza...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： An Duy Nguyen Chuong Phuoc Le Pratik Walunj Anton Netchaev Thanh Nho Do Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA Graduate School of Biomedical Engineering UNSW Sydney Kensington Campus NSW Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a comprehensive LiDAR-inertial odometry framework featuring robust smoothing and mapping capabilities, effectively correcting LiDAR feature point skewness using an inertial measurement unit (IMU). While the Extended Kalman Filter (EKF) is a common choice for nonlinear motion estimation, its complexity grows when handling maneuvering targets. To overcome this challenge, a new framework that incorporates the Iterated Interactive Multiple Models of Kalman Filter (IMMKF) is given, providing a solution for reliable navigation in dynamic motion and noisy conditions. To ensure map consistency, an ikd-tree that facilitates continuous updates and adaptive rebalance is employed, preserving the map’s integrity. To guarantee the robustness of our approach, it undergoes extensive testing across diverse scales of indoor and outdoor environments. This testing scenario simulates absolute GPS denial. In terms of estimated motion, the new algorithm demonstrates superior accuracy compared to existing approaches. The implementation is openly accessible on GitHub 4 for further exploration.

关键词： Location awareness Technological innovation Laser radar Accuracy Smoothing methods Computational modeling Feature extraction Odometry Kalman filters Testing

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Constrained Generative Sampling of 6-DoF Grasps

Constrained Generative Sampling of 6-DoF Grasps

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jens Lundell Francesco Verdoja Tran Nguyen Le Arsalan Mousavian Dieter Fox Ville Kyrki KTH Royal Institute of Technology Sweden Department of Electrical Engineering and Automation Intelligent Robotics Group School of Electrical Engineering Aalto University Finland NVIDIA Corporation USA Paul G. Allen School of Computer Science & Engineering University of Washington Seattle USA

Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable grasps is sufficient. However, for completing specific tasks, such as squeezing out liquid from a bottle, we want the grasp to be on a specific part of the object's body while avoiding other locations, such as the cap. This work presents a generative grasp sampling network, VCGS, capable of constrained 6-Degrees of Freedom (DoF) grasp sampling. In addition, we also curate a new dataset designed to train and evaluate methods for constrained grasping. The new dataset, called CONG, consists of over 14 million training samples of synthetically rendered point clouds and grasps at random target areas on 2889 objects. VCGS is benchmarked against GraspNet, a state-of-the-art unconstrained grasp sampler, in simulation and on a real robot. The results demonstrate that VCGS achieves a 10-15% higher grasp success rate than the baseline while being 2–3 times as sample efficient. Supplementary material is available on our project website.

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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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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

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. © 2023 IEEE.

关键词： Fish

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MROS: A framework for robot self-adaptation

arXiv

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arXiv 2023年

作者： Silva, Gustavo Rezende Bozhinoski, Darko Oviedo, Mario Garzon Montero, Mariano Ramírez Garcia, Nadia Hammoudeh Deshpande, Harshavardhan Wasowski, Andrzej Corbato, Carlos Hernandez The Department of Cognitive Robotics Delft University of Technology Delft Netherlands IRIDIA University Libre du Bruxelles Bruxelles Belgium Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Computer Science Department IT University Copenhagen Denmark

Self-adaptation can be used in robotics to increase system robustness and reliability. This work describes the Metacontrol method for self-adaptation in robotics. Particularly, it details how the MROS (Metacontrol for ROS Systems) framework implements and packages Metacontrol, and it demonstrate how MROS can be applied in a navigation scenario where a mobile robot navigates in a factory floor. Video: https://***/watch?v=ISe9aMskJuE © 2023, CC BY.

关键词： Mobile robots

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An Assembly Sequence Planning Method Using Ant Colony Algorithm for Arbitrary Placed Workpieces

An Assembly Sequence Planning Method Using Ant Colony Algori...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Hou, Ruiming Xu, Sheng Yang, Chenguang Zhu, Bing Duan, Jianghua Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Science Beijing101408 China Shandong Institute of Advanced Technology Cas Shandong250102 China South China University of Technology College of Automation Science and Engineering Guangzhou510640 China Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350327182

This paper proposes a particular and effective online sequence planning method to help manipulators complete complex assembly tasks. In many existing studies, the workpiece is constrained to be placed at a particular initial position. To overcome this drawback, we combine the ant colony algorithm with a target detection algorithm, which could get the positions of the workpieces in real time for online sequence planning. In addition, the constraint and obstacle avoidance of different workpieces in the assembly process is also considered. According to the experiment results, an observation is found that the new method is superior to the general way in terms of efficiency and cost function value. Furthermore, the proposed method effectively solves different practical and feasible assembly sequence planning problems. © 2023 IEEE.

关键词： Ant colony optimization

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Gender and Age based Census System for Metropolitan Cities 8

Gender and Age based Census System for Metropolitan Cities

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8th IEEE International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2020

作者： Mittal, Shiva Rajput, Vikram Singh Ajay Kumar Garg Engineering College Ghaziabad AKTU Department of Automation Robotics Engineering Uttar Pradesh India

ISBN: (纸本)9781728170169

This paper presents a smart computer-vision based system which helps ensure disciplined entry of people in places by ascertaining the allowable age and gender of the person, as well as storing the data for census use. The system involves deep learning based facial image classifiers, which predict the gender and age from the image of face captured by the camera in real-time. The actuating response is generated according to the prediction of the computer vision algorithm, which further keeps the record of encountered faces with the respective counts of persons with corresponding facial traits. The log of the entire surveillance process can be monitored online through the integrated IOT capability, thereby can be used as primary data source. The successful experimental runs demonstrate the system's usability in the real world applications. © 2020 IEEE.

关键词： Computer vision

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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Analysis of ECG Noise Cancellation and Abnormality Classification using Various Adaptive Algorithms

Analysis of ECG Noise Cancellation and Abnormality Classific...

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Applied Intelligence and Sustainable Computing (ICAISC), International Conference on

作者： S M Ramesh S Nithya V. V. Teresa V Uma Maheswari M A Raja S Krishnakumar Department of ECE KPR Institute of Engineering and Technology Coimbatore India Dept. of ECE Sri Eshwar College of Engineering Coimbatore India Dept. of ECE Kumaraguru College of Technology Coimbatore India Department of ECE Nehru Institute of Engineering and Technology Coimbatore India Department of Robotics and Automation Sri Ramkrishna Engineering College Coimbatore India

Noise cancellation and abnormality detection in ECG signal is a tedious task in real time environment. These noises arise due to power line interference and human movements. There are various digital filters readily available to reduce noise in ECG signal but the computational cost and complexity is high. To overcome this drawback, adaptive filters are introduced and abnormal ECG signal is also detected using adaptive algorithms. This paper talks about multilevel pipelining and cutest retiming techniques in adaptive filters, for example, LMS (Least mean square), BLMS (Block LMS), NLMS (Normalized LMS), BNLMS (Block NLMS), VSLMS (Variable advance size LMS) and BVSLMS (Block VSLMS) calculations. This paper also classifies various abnormal ECG signal and detects the type of disease. It is observed that there is 15% increase in SNR and 3% reduction in MSE compared to the conventional algorithms.

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Sequential Manipulation Planning for Over-Actuated Unmanned Aerial Manipulators

Sequential Manipulation Planning for Over-Actuated Unmanned ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yao Su Jiarui Li Ziyuan Jiao Meng Wang Chi Chu Hang Li Yixin Zhu Hangxin Liu National Key Laboratory of General Artificial Intelligence Beijing Institute for General Artificial Intelligence (BIGAI) Department of Advanced Manufacturing and Robotics College of Engineering Peking University Department of Automation Tsinghua University Institute for Artificial Intelligence Peking University

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.

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