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Model Design and Concept of Operations of Standard Interface for On-orbit Construction

Model Design and Concept of Operations of Standard Interface...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Jingdong Zhao Zirui Wang Ziyi Liu Liangliang Zhao Qifan Duan Hong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Chang Guang Satellite Technology Co. Ltd. Changchun China Beijing Honor Device Co. Ltd. Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The construction of large-scale space facilities requires the use of on-orbit construction technology. However, several of its key components, such as standard interface design, compliant control methods, and path planning for multi-branch robots, still need improvement before practical application. This paper presents a comprehensive solution for on-orbit construction tasks, encompassing a novel standard interface, docking control method, and path planning method for space multi-branch robots. Firstly, a novel standard interface is introduced, which features multiple mating modes and a lightweight design. Additionally, a compliant docking method is provided to generate lower contact forces along the Z-direction. Furthermore, for four-armed space robots, a hierarchical planning method is proposed, which innovates in environment map construction and locomotion planning. Specifically, the closed-form Minkowski sum method is employed to solve the robot’s free space, and a concise locomotion method is elucidated based on transition support points. Finally, simulations and experiments are conducted.

关键词： Visualization Machine vision Prototypes Aerospace electronics Path planning Planning Task analysis

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Real-time Optimal Navigation Planning Using Learned Motion Costs

Real-time Optimal Navigation Planning Using Learned Motion C...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yang, Bowen Wellhausen, Lorenz Miki, Takahiro Liu, Ming Hutter, Marco Hong Kong Univ Sci & Technol Robot Inst Robot & Multipercept Lab Hong Kong Peoples R China Swiss Fed Inst Technol Robot Syst Lab Zurich Switzerland

ISBN: (纸本)9781728190778

Navigation on challenging terrain topographies requires the understanding of robots' locomotion capabilities to produce optimal solutions. We present an integrated framework for real-time autonomous navigation of mobile robots based on elevation maps. The framework performs rapid global path planning and optimization that is aware of the locomotion capabilities of the robot. A GPU-aided, sampling-based path planner combined with a gradient-based path optimizer provides optimal paths by using a neural network-based locomotion cost predictor which is trained in simulation. We show that our approach is capable of planning and optimizing paths three orders of magnitude faster than RRT* on GPU-enabled hardware, enabling real-time deployment on mobile platforms. We successfully evaluate the framework on the ANYmal C quadrupedal robot in both simulations and real-world environments for path planning tasks on multiple complex terrains.

关键词： Costs Navigation Computational modeling Training data Surfaces Sampling methods Real-time systems

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Generation-based Code Review automation: How Far Are Weƒ 31

Generation-based Code Review Automation: How Far Are Weƒ

引用

31st IEEE/ACM international conference on Program Comprehension, ICPC 2023

作者： Zhou, Xin Kim, Kisub Xu, Bowen Han, Donggyun He, Junda Lo, David Singapore Management University Singapore Royal Holloway University of London United Kingdom

ISBN: (纸本)9798350337501

Code review is an effective software quality assurance activity;however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review comment generation).We find the previous works carry three main limitations. First, the ACR approaches have been shown to be beneficial in each work, but those methods are not comprehensively compared with each other to show their superiority over their peer ACR approaches. Second, general-purpose pre-trained models such as CodeT5 are proven to be effective in a wide range of Software Engineering (SE) tasks. However, no prior work has investigated the effectiveness of these models in ACR tasks yet. Third, prior works heavily rely on the Exact Match (EM) metric which only focuses on the perfect predictions and ignores the positive progress made by incomplete answers. To fill such a research gap, we conduct a comprehensive study by comparing the effectiveness of recent ACR tools as well as the general-purpose pre-trained models. The results show that a general-purpose pre-trained model CodeT5 can outperform other models in most cases. Specifically, CodeT5 outperforms the prior state-of-the-art by 13.4%-38.9% in two code revision generation tasks. In addition, we introduce a new metric namely Edit Progress (EP) to quantify the partial progress made by ACR tools. The results show that the rankings of models for each task could be changed according to whether EM or EP is being utilized. Lastly, we derive several insightful lessons from the experimental results and reveal future research directions for generation-based code review automation. © 2023 IEEE.

关键词： Computer software selection and evaluation

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Attention Regulation for Efficient Semantic Segmentation on Unstructured Terrain

Attention Regulation for Efficient Semantic Segmentation on ...

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international conference on Control, automation and Systems ( ICCAS)

作者： Xiujin Liu Feng Xue Xiaoxiao Du Department of Robotics University of Michigan Ann Arbor MI USA

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

We present AR-Net, an efficient semantic segmentation pipeline for unstructured terrains. For applications such as autonomous navigation, it is essential to accurately and efficiently understand the unstructured scenes in outdoor and urban environments. Given RGB images as inputs, the AR-Net uses an encoder backbone to extract multi-scale features and a novel Attention-Regulation layer as part of the decoder to predict the pixel-level segmentation results for unstructured terrains. Our AR-Net model achieved superior segmentation performance and fast inference on two real-world outdoor terrain datasets. We also provide detailed ablation studies and analyses on model parameter selections.

关键词： Analytical models Accuracy Simultaneous localization and mapping Semantic segmentation Urban areas Refining Feature extraction Robustness Regulation Real-time systems

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Review on Reinforcement Learning Controller in Soft Manipulator

Review on Reinforcement Learning Controller in Soft Manipula...

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2021 IEEE international conference on robotics and Biomimetics, ROBIO 2021

作者： Wang, Shuopeng Wang, Rixin Liu, Meng Zhang, Ying Hao, Lina Northeastern University School of Mechanical Engineering Automation Shenyang110819 China

ISBN: (纸本)9781665405355

Soft manipulator is a special type of robot that can achieve flexible deformation. It is mostly made of soft materials, so it has the characteristics of high flexibility and high task adaptability. This paper combines the working characteristics of soft manipulators and summarizes the application of reinforcement learning on soft manipulators, including the application based on the modeling of environment and the application in the physical robot platform. Based on the current status, the development prospects for the application of reinforcement learning controllers in soft manipulators are discussed. Besides, based on the characteristics of the algorithm in different situations, this paper analyzes and proposes feasible methods in use. © 2021 IEEE.

关键词： Reinforcement learning

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Leveraging Large Language models for Enhancing Financial Compliance: A Focus on Anti-Money Laundering Applications

Leveraging Large Language Models for Enhancing Financial Com...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Yuqi Yan Tiechuan Hu Wenbo Zhu Olin Business School Washington University in St. Louis Saint Louis MO Department of Computer Science John Hopkins University Baltimore MD Physical Science Division University of Chicago Chicago IL

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

Money laundering is dangerous to the world's finan-cial institutions, because it allows hiding illegal cash and supports various criminal activities. Due to reliance on manual procedures and rule-based systems, traditional Anti-Money Laundering (AML) techniques need assistance in managing the complexity and volume of contemporary financial transactions. This research explores how Large Language models (LLMs), such as GPT-3 and BERT, can improve AML by utilizing cutting-edge natural language processing capabilities to provide more reliable and scalable money laundering detection and prevention solutions. By examining the transformational potential of LLMs in overcoming the drawbacks of conventional techniques, the research fills a significant vacuum in the present AML procedures. Enhancing transaction monitoring, refining client due diligence procedures, streamlining sanctions screening, and aiding in the production of precise suspicious activity notifications are among the primary study issues. The main assumption is that utilizing LLMs' capacity to handle unstructured data and spot complex patterns, AML accuracy and efficiency will significantly increase compared to traditional systems. The goals include implementing LLMs across AML functions, assessing their effectiveness with various datasets, as well as presenting empirical research and case studies to prove their superiority. By advancing our knowledge of and ability to use LLMs in financial compliance, this research is expected to strengthen the defenses against financial crimes.

关键词： Large language models Prevention and mitigation Refining Vacuum technology Production Manuals Natural language processing Reliability Robots Monitoring

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Research on Salt Pool Ion Concentration Prediction Model Based on LSTM and Attention Mechanism

Research on Salt Pool Ion Concentration Prediction Model Bas...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： Shaocheng Qu Xiaocheng Xiang Haiyang Tang Guohong Yi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Center of Technology SDIC Xinjiang Luobupo Hoevellite Co. Ltd Hami China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

The Lop Nur Salt Lake in Xinjiang is rich in underground brine resources and serves as an important potassium fertilizer production area in China. Potassium, an essential nutrient for plant growth, not only improves plant quality but also enhances their resistance to lodging, drought, cold, and lodging, playing a crucial role in agricultural production. In the process of ion collection in Lop Nur Salt Lake, the predicted ion concentration serves as a significant reference for guiding the industrial automation of salt mining machines. The predicted ion concentration is not only a part of automated mining but also a crucial step in the automated separation and purification of potassium salt crystals, as the ion concentration at the collection site directly affects the method of impurity removal. The predicted ion concentration can guide the automated production of potassium salt, and its accuracy directly impacts the efficiency of automated potassium salt production. To improve the accuracy of current salt pond ion concentration predictions and reduce prediction costs, this paper proposes a model integrating LSTM and attention mechanisms for time series prediction. We collected measurement data for various ion concentrations such as K + , Na + , $\text{SO}_4^{2 -}$, Cl − , and Mg 2+ using ion concentration measurement sensors, recording features such as collection time, location coordinates, salt field number, temperature, and air humidity. By training and testing the sample set using different models and evaluating and comparing the experimental results using various performance evaluation metrics, we found that the proposed model can effectively model long time series data, outperforming RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in terms of prediction performance.

关键词： Temperature measurement Salt Attention mechanisms Time series analysis Production Predictive models Ions Data models Potassium Long short term memory

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Analysis of Motor Imagery-Based fNIRS-BCI for Knee Flexion and Extension

Analysis of Motor Imagery-Based fNIRS-BCI for Knee Flexion a...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Hamza Shabbir Minhas Hammad Nazeer Noman Naseer Iraj Kainat Haroon Khan Peyman Mirtaheri Department of Mechatronics and Biomedical Engineering Air University Islamabad Pakistan Department of Mechanical Electronic and Chemical Engineering OsloMet - Oslo Metropolitan University Oslo Norway

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This study aimed to explore the potential of functional near-infrared spectroscopy (fNIRS) as a non-invasive technique for detecting hemodynamic responses in cortical brain activities during motor imagery (MI) tasks. Specifically, the study focused on classifying multiclass MI tasks with fNIRS data. A total of 10 healthy individuals participated in the experiment, where they were instructed to perform motor imagery tasks. A multi-channel continuous wave fNIRS system records the signals from the motor cortex for knee flexion and extension imagery tasks. Discriminant Analysis (LDA) and Support Vector Machine (SVM), comparatively computationally efficient, algorithms were used to classify the data for fNIRS-based BCI applications. The SVM classifier performed comparatively better than the LDA classifier in accuracy, with 88.8% and 89.4% for two-feature and three-feature combinations. Brain activation maps show the increased activation for flexion movement of the knee. Future research should focus on refining the classification process through feature selection, preprocessing improvements, and ensemble methods to enhance accuracy.

关键词： Support vector machines Industries Limbs Accuracy Neuromuscular Refining Motors Hemodynamics Functional near-infrared spectroscopy robotics and automation

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Depth Recognition of Hard Inclusions in Tissue Phantoms for Robotic Palpation

Depth Recognition of Hard Inclusions in Tissue Phantoms for ...

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2022 IEEE international conference on Real-Time Computing and robotics, RCAR 2022

作者： Zhou, Zhenning Fang, Senlin Ye, Chaoxiang Mi, Tingting Huang, Binhua Li, Xiaoyu Yi, Zhengkun Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT Branch Shenzhen518055 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Medical palpation is an effective diagnosis method in which physicians use tactile sensation to diagnose a patient's pathology. Robotic palpation is a novel technique that leverages robots to assist medical diagnosis. The problem of tactile information loss in Robot-assisted Minimally Invasive Surgery (RMIS) has limited the development of the robot-assisted surgical system. Meanwhile, surgeons are difficult to acquire some key information about lesions only via visual feedback, such as tumor depth. To address the issue, we propose a tactile perception algorithm on the basis of the CNN-LSTM network, which achieves the depth recognition of hard inclusions in tissue phantoms. The method realizes the classification of twelve depths of hard inclusions. In addition, due to using hypergeometric distribution encoding, the proposed method can exploit the ordinal information of the labels to significantly improve the recognition accuracy rate. The experimental results on 720 real tactile data show that the average recognition rate is 96.45%. Compared with other state-of-the-art methods, the recognition accuracy rate of the proposed algorithm is the highest. © 2022 IEEE.

关键词： Tumors

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Glass Objects Detection Based on Transformer Encoder-Decoder 6

Glass Objects Detection Based on Transformer Encoder-Decoder

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6th international conference on automation, Control and Robots, ICACR 2022

作者： Hou, Xiaonan Zhan, Minghao Wang, Chunlei Fan, Chunhui The 21st Research Institute of China Electronics Technology Group Corporation Robotics Engineering Center Shanghai China

ISBN: (数字)9781665498739

ISBN: (纸本)9781665498739

Glass objects detection is a relatively new and challenging work in the field of computer vision. This work will also have an impact on other visual tasks, such as depth estimation, instance segmentation and saliency detection. Compared with using high-precision sensors for glass object detection, the vision based detection method is more economical and robust. In this paper, we focus on the task of glass object detection in the field of image segmentation. Traditional semantic segmentation methods mostly used the model based on convolutional neural network to complete the task. On this basis, we introduce the Transformer encoder-decoder architecture to expand the size of receptive field, make full use of different levels of context information and improve the detection effect of glass objects. In this paper, the mixed model of convolutional neural network and Transformer is used to realize the detection of glass objects in the Pytorch environment, and the model is trained and evaluated on the Trans10K-v2 dataset. In the experiment, the input image resolution is 512 × 512, the final pixel accuracy is 93.94%, and the mean intersection over union is 71.32%. © 2022 IEEE.

关键词： Object detection

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