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A Hybrid Admittance Control Algorithm for Automatic Robotic Cranium-Milling

A Hybrid Admittance Control Algorithm for Automatic Robotic ...

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IEEE international conference on robotics and automation (ICRA)

作者： Chen Qian Zhen Li Qiang Ye Peicong Ge Jizong Zhao Gui-Bin Bian Institute of Automation Chinese Academy of Sciences Beijing China China National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Prior robot-assisted cranium-milling studies only considered controlling the force in the skull’s vertical direction and neglected the milling cutter’s feed force. Additionally, achieving stable force control in multiple directions is challenging for robots due to the uneven skull surface. Here a hybrid admittance control algorithm incorporating a model-free adaptive nonlinear force control and fuzzy control algorithms is proposed to accomplish effective automatic cranial-milling tasks. First, a pure data-driven model-free adaptive control method based on partial form dynamic linearization is used to control the feed force. Second, fuzzy control minimizes the total error of both the vertical and feed force by adaptively adjusting the milling cutter’s velocity and position. 42 ex vivo animal skull-milling experiments conducted by the automatic robotic cranium-milling system indicate that when using the proposed control algorithm, the force error percentage can be maintained below 5.0% within 3 s and the maximal root mean square error percentages for vertical and feed force are 1.85% and 1.94%, respectively. Moreover, no instances of dura mater damage are observed and the robotic system exhibits a high level of autonomy as it performs the skull milling task with minimal human involvement throughout the entire experiment. The results suggest the potential for advancing the intelligence level of neurosurgery in the future.

关键词： Fuzzy control Adaptation models Force Milling Skull Feeds Task analysis

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MF-MOS: A Motion-Focused Model for Moving Object Segmentation

MF-MOS: A Motion-Focused Model for Moving Object Segmentatio...

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IEEE international conference on robotics and automation (ICRA)

作者： Jintao Cheng Kang Zeng Zhuoxu Huang Xiaoyu Tang Jin Wu Chengxi Zhang Xieyuanli Chen Rui Fan School of Electronic and Information Engineering South China Normal University Foshan China Department of Computer Science Aberystwyth University Aberystwyth U.K. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Internet of Things Engineering Jiangnan University Wuxi China College of Intelligence Science and Technology National University of Defense Technology Changsha China College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://***/SCNU-RISLAB/MF-MOS.

关键词： Laser radar Semantics Dynamics Object segmentation Data augmentation Data models Reliability

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Kalman Filter Adaptive Speed Tracking Control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE international conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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An Interval Shrinking Trust Region Algorithm for GNSS/eLoran Pseudorange Fusion Positioning Initialization

An Interval Shrinking Trust Region Algorithm for GNSS/eLoran...

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automation, robotics and Computer Engineering (ICARCE), international conference on

作者： Kaiqi Liu Wenhe Yan Jiangbin Yuan Zaihui Xiao Chaozhong Yang Yu Hua Chinese Academy of Sciences National Time Service Center Xi’an China Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Laboratory of Precise Positioning and Timing Technology Xi’an China Chinese Academy of Sciences National Time Service Cente Key Laboratory of Precise Positioning and Timing Technology Xi’an China

ISBN: (纸本)9781665475495

Fusion of Global Navigation Satellite System (GNSS) with eLoran can greatly improve the coverage and availability of Positioning Navigation and Timing (PNT) services. However, due to the strong nonlinearity in the eLoran pseudorange equations, traditional methods may fail to converge or converge incorrectly when solving the fused pseudorange equation system. In this paper, an interval shrinking trust region algorithm (ISTR) is proposed to solve the pseudorange-based eLoran/GNSS fusion positioning problem, especially when no reliable initial values are available. Simulation and measured data verify that the ISTR algorithm can help the receiver to complete reliable positioning initialization without reliable initial values, and the initialization success rate is 100%.

关键词： Global navigation satellite system Receivers Interference Position measurement Mathematical models Data models Timing

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Decision-making strategy for multi-agents using a probabilistic approach: application in soccer robotics

Decision-making strategy for multi-agents using a probabilis...

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international conference on Control, automation and Information Sciences (ICCAIS)

作者： Hoang Anh Pham Valentin Gies Thierry Soriano University of Toulon Toulon France

The efficient coordination of soccer robots is a complex topic because there are numerous possible scenarios in the game, and the state of the robots can change rapidly. It requires the robot to be able to analyze and make decisions in a short time. In this article, we first use Dec-POMDP to describe the actions and states of the soccer robot team. Secondly, we introduce a probabilistic approach so that the robots can quickly make decisions corresponding to specific situations. More specifically, we present a method for calculating and evaluating the expected points corresponding to each particular action for each robot. The robots then choose the actions with the highest expected points. Finally, we have developed a simulator based on the digital twin approach to verify the proposed strategies on simulation models and implement these strategies rapidly on real robots.

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Evaluation of a Data-Driven Model for Deformable Linear Objects Manipulated by Two Robot Manipulators Using Experimental Data

Evaluation of a Data-Driven Model for Deformable Linear Obje...

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international conference on Control, automation and Systems ( ICCAS)

作者： Jiyoung Choi G. Micheale Haileslassie Geon Kim Ayoung Hong Department of Mechanical Engineering Chonnam National University Gwangju South Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Deformable objects such as cables, ropes, and cloth-like objects are more commonly used in our daily lives rather than rigid bodies. However, manipulating these deformable objects with a robot is challenging due to their high degrees of freedom and complex deformations, making it difficult to generate dynamic models using traditional analytical methods. Data-driven models have been employed to develop dynamic models for such deformable objects. However, to learn the models from experimental data, it is recommended to reduce the number of variables. In this paper, we established an experimental environment where deformable linear objects (DLOs) can be manipulated using a dual-arm robot. We evaluated the performance of the data-driven model using the positional information of the objects obtained in this setup. We achieved an average position error of 0.010 m for forward prediction and 0.073 m for long-term prediction. In future research, we intend to employ this data-driven model to manipulate cables into desired shapes.

关键词： Deformable models Solid modeling automation Shape Deformation Predictive models Control systems Data models Manipulator dynamics Cables

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GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search

GUTS: Generalized Uncertainty-Aware Thompson Sampling for Mu...

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IEEE international conference on robotics and automation (ICRA)

作者： Nikhil Angad Bakshi Tejus Gupta Ramina Ghods Jeff Schneider Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh PA

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation addresses the requirement that search missions should focus on quick recovery of OO1s rather than full coverage of the search region. Previous approaches fail to accurately model sensing uncertainty, account for occlusions due to foliage or terrain, or consider the requirement for heterogeneous search teams and robustness to hardware and communication failures. We present the Generalized Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these issues and is suitable for deployment on heterogeneous multi-robot systems for active search in large unstructured environments. We show through simulation experiments that GUTS consistently outperforms existing methods such as parallelized Thompson Sampling and exhaustive search, recovering all OOIs in 80% of all runs. In contrast, existing approaches recover all OOIs in less than 40% of all runs. We conduct field tests using our multirobot system in an unstructured environment with a search area of ≈75,000 m 2 . Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field run, and significantly outperforming our baseline.

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Demand Response based Zonal Load Forecasting for Electric Vehicle Charging Stations 2

Demand Response based Zonal Load Forecasting for Electric Ve...

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2nd international conference on Mechanical, Electronics, and Electrical and automation Control, METMS 2022

作者： Wang, Huimin College Of Automation College Of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781510654785

ISBN: (纸本)9781510654778

With the access to broad-based demand response (DR) resources such as large-scale electric vehicles (EVs), customers are responding to demand under the influence of various incentives in the electricity market, which will change load characteristics and affect load forecasting. This paper proposes a zonal load forecasting for EVs based on DR, using wavelet decomposition(WD) to form seasonal base load and DR dominant part load for similar days, dividing the area where EV charging stations are located into residential, work and public zones, using support vector machine regression methods to forecast for residential and work zones, and using time series models to forecast for seasonal For the public area, a time series model is used to forecast the seasonal base load, and a grey forecasting model is used to forecast the dominant part of the DR, and Markov correction is applied to the forecast results. By comparing with the traditional method, the proposed method proves its effectiveness and accuracy in forecasting the load of EV charging stations with demand response. © 2022 SPIE

关键词： Electric vehicles

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Harnessing Large Language models for Temporal Knowledge Graph Forecasting with Multi-view Prompting

Harnessing Large Language Models for Temporal Knowledge Grap...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Da Zhang Tong Xu Fake Lin Jiayue Chen University of Science and Technology of China Hefei China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

In recent years, Temporal Knowledge Graphs (TKGs) have attracted considerable attention from researchers. An important research direction is forecasting future events in TKGs based on historical information, i.e. TKG forecasting. Existing methods primarily rely on modeling temporal relationships between entity IDs to make predictions, which brings limitations in understanding the entity content and the relationships between entities. Fortunately, the emergence of Large Language models (LLMs) has brought new insights into solving this problem. In this paper, we propose a novel LLM-enhAnced TKG forEcasting (LATE) model. Specifically, we first design multi-view prompts that enable LLMs to generate comprehensive descriptions for entities as external knowledge. Then, we get the enhanced representations of entities based on the descriptions and we further design a contrastive learning strategy to reinforce this process. Finally, we construct a TKG forecasting model based on self-attention mechanisms and dual-pattern LSTM-based temporal modeling to accomplish the forecasting task. Experiments on three datasets demonstrate the effectiveness and superiority of our model compared with several state-of-the-art baseline methods.

关键词： Large language models Noise Knowledge graphs Learning (artificial intelligence) Contrastive learning Bidirectional control Predictive models Encoding Forecasting Robots

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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Networks 24th

Cost-Efficient Multi-Objective Design of Miniaturized Microw...

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24th international conference on Computational Science (ICCS)

作者： Koziel, Slawomir Pietrenko-Dabrowska, Anna Leifsson, Leifur Reykjavik Univ Engn Optimizat & Modeling Ctr Dept Engn Menntavegur 1 IS-102 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat Narutowicza 11-12 PL-80233 Gdansk Poland Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

ISBN: (纸本)9783031637742;9783031637759

Designing microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when executed with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective MO technique for microwave passive components utilizing a machine learning (ML) framework with artificial neural network (ANN) surrogates as the primary prediction tool. In this approach, multiple candidate solutions are extracted from the Pareto set via optimization using a multi-objective evolutionary algorithm (MOEA) applied to the current ANN model. These solutions expand the dataset of available (EM-simulated) parameter vectors and refine the surrogate model iteratively. To enhance computational efficiency, we employ variable-resolution EM models. Tested on two microstrip circuits, our methodology competes effectively against several surrogate-based approaches. The average computational cost of the algorithm is below three hundred EM analyses of the circuit, with the quality of generated Pareto sets surpassing those produced by the benchmark methods.

关键词： Microwave design multi-objective optimization design automation machine learning neural networks surrogate modeling Pareto optimality

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