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检索条件"任意字段=3rd International Conference on Informatics in Control, Automation and Robotics"

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Real-Time Display of Dense Neuronal Activation Map Using Functional Near-Infrared Spectroscopy 3

Real-Time Display of Dense Neuronal Activation Map Using Fun...

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3rd international conference on robotics and automation in Industry (ICRAI)

作者： Yaqub, M. Atif Ghafoor, Usman Hong, Keum-Shik Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

ISBN: (纸本)9781728130583

The risk of getting psychiatric disease or various kinds of dementia is rising as a significant problem in the aging society. Functional near-infrared spectroscopy (fNIRS) can measure the blood chromophores noninvasively for early diagnosis, and frequent examination, which are vital in case of brain degeneration. We present the development and functioning of our lab-developed fNIRS system. It provides a real-time display of variation in blood chromophores related to neuronal activation. It employs 128 dual-wavelength LEDs of 735 nm and 850 nm. The selection of these wavelengths allows computation of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). A single photosensor is used in the presented device. This system is developed using a modular approach where a single module can cover approximately 7 cm x 7 cm while multiple modules can be used to cover a wider area. The current configuration utilizes different source-detector separation to reach multiple depths between 2 cm and 3.5 cm. Short separation channels also exist in the design to provide the information of superficial layers. MOSFET based LED switching is implemented that allows sharp current switching for high-speed data acquisition. Windows-based software is developed for the display of fNIRS data in real time. Wi-Fi is used as the wireless medium of communication between the hardware and software. Phantom model, as well as human subject, was used for testing the device efficacy. The phantom results showed that by increasing the channel-separation, the signal intensity was reduced. Resting state human subject was also evaluated to compute and display the HbO in real time. A complete fNIRS sample comprising of 128 channels was recorded in 25 ms.

关键词： fNIRS near-infrared Spectroscopy neuroimaging 3D brain map system development

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Soft Robot control With a Learned Differentiable Model

Soft Robot Control With a Learned Differentiable Model

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IEEE international conference on Soft robotics (RoboSoft)

作者： James M. Bern Yannick Schnider Pol Banzet Nitish Kumar Stelian Coros Computational Robotics Lab in the Institute for Intelligent Interactive Systems (IIIS) ETH Zurich Switzerland

ISBN: (数字)9781728165707

ISBN: (纸本)9781728165714

Soft robots are inherently safe and comply readily to their environment. They are therefore exciting for applications like search and rescue or medicine, which involve a high degree of uncertainty, and require interacting with humans. However, the best way to model and control soft robots largely remains an open question. One promising approach is to leverage physically-based modeling techniques such as the finite element method. However, such techniques are inherently limited by their physical assumptions. Indeed, real-world soft robots are often made from unpredictable materials, using imprecise techniques. Data-driven approaches provide an exciting alternative, as they can learn real-world fabrication defects and asymmetries. In this paper we present our first investigation into using machine learning to do soft robot control. We learn a differentiable model of a soft robot's quasi-static physics, and then perform gradient-based optimization to find optimal open-loop control inputs. We find that our learned model captures phenomena that would be absent from an idealized physically-based simulation. We also present practical techniques for acquiring high-quality motion capture data, and observations the effect of network complexity on model accuracy.

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robotics based Solution for Data Center E-Monitoring 3

Robotics based Solution for Data Center E-Monitoring

引用

3rd international conference on Advanced Systems and Emergent Technologies, IC_ASET 2019

作者： Terrissa, Labib Sadek Ayad, Soheyb Zerhouni, Noureddine Dept. CS LINFI Laboratory University of Biskra Biskra Algeria AS2M PHM Femto-ST ENSMM Besanon France

ISBN: (纸本)9781728113173

In this paper, we present a Cloud robotics (CR) platform for performing environmental monitoring in data centers. Due to the high energy consumption, the need of monitoring for controlling air temperature in each data center rooms has been the critical subject in recent years. This paper presents a cloud robotics platform based on the Robot Operating System (ROS), in which a mobile robot is able to autonomously navigate using a previously created map in a data center room for accurate monitoring of critical measurements, localize therein and execute a list of measurements at different locations, which are provided by the user via a Graphical User Interface(GUI). This paper discusses the implementation and simulated results during an experimental scenario. © 2019 IEEE.

关键词： Mobile robots

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An uncertain multi-fingered robot host system based on parallel LM and multiprocessor 3

An uncertain multi-fingered robot host system based on paral...

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3rd IEEE international conference on Electronic Information Technology and Computer Engineering, EITCE 2019

作者： Wang, Li Pu, Yunming Wei, Wang You, Zhining Jimei University Computer Engineering College Xiamen China

ISBN: (纸本)9781728135847

Due to the nonlinearity and complexity of uncertain multi-finger robot, the ordinary host system is difficult to control effectively. An uncertain multi-finger robot host system based on multiprocessor (MP) and LM algorithm was proposed, which improves the system responsiveness with MP. Also a core algorithm base on LM was built to check the effect of MP by handling the parallel processing of 3 fitting function models, including fitting function parallelization, Jacobi method parallelization, Gaussian elimination parallelization. The experiment results show that MP can improve the performance of LM algorithms, and promote the capacity and responsiveness of the host system. © 2019 IEEE.

关键词： robotics

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Optimization of Distance Between Magnets for Magnetic Wall Climbing Robots

Optimization of Distance Between Magnets for Magnetic Wall C...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Jaise Jose R. Sugin Elankavi D. Dinakaran RM. Kuppan Chetty M.M. Ramya Centre for Automation and Robotics (ANRO) School of Mechanical Sciences Hindustan Institute of Technology and Science Chennai Tamil Nadu

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

In this paper, an adhesion system used for magnetic wall climbing robots using permanent magnets is examined for its change in adhesion concerning the inter-magnet distance within the Yoke. Pocket milled ferrous material of high permeability named yoke is used to hold neodymium magnets in mobile wall-climbing robots. For a climbing robot, the self-weight is a significant parameter which needs to be at the minimum possible value. This simulation study is expected to help in bringing down the weight of the yoke holding magnets and help in finding the right inter magnet distance for the magnets used. Simulation studies were carried out for various inter magnet distances and the values are reported. An optimized distance was arrived using the simulation results thus obtained.

关键词： Magnetic flux Adhesives Saturation magnetization Robots Magnetic resonance imaging Magnetostatics Permanent magnets

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Action Recognition with 3D ConvNet-GRU Architecture 18

Action Recognition with 3D ConvNet-GRU Architecture

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3rd international conference on robotics, control and automation (ICRCA) / 3rd international conference on robotics and Machine Vision (ICRMV)

作者： Yao, Guangle Liu, Xianyuan Lei, Tao Chinese Acad Sci Inst Opt & Elect Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450365307

Video action recognition is widely applied in video indexing, intelligent surveil-lance, multimedia understanding, and other fields. Recently, it was greatly improved by incorporating the learning of deep information using convolutional neural network (ConvNet). In this paper, we proposed a 3D ConvNet-GRU architecture to learn deep information for action recognition. Specifically, we use 3D ConvNet to learn spatiotemporal information from short RGB clips and optical flow clips, and impose gated recurrent unit (GRU) on the spatiotemporal information to model the temporal evolution for action recognition. The experimental results show that our 3D ConvNet-GRU method is effective to model temporal evolution for action and achieves recognition performance comparable to that of state-of-the-art methods.

关键词： Action Recognition Gated Recurrent Unit Convolutional Neural Network Spatiotemporal Information

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A Discriminatively Learning Model with Illumination Transfer for Inter-Camera Pedestrians Association 18

A Discriminatively Learning Model with Illumination Transfer...

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3rd international conference on robotics, control and automation (ICRCA) / 3rd international conference on robotics and Machine Vision (ICRMV)

作者： Zhong, Shijun Yu, Chunyan Lin, Jiali Fuzhou Univ Xueyuan Rd 2 Fuzhou Fujian Peoples R China

ISBN: (纸本)9781450365307

Inter-camera pedestrians association always employs appearance features to merge tractlets of the same pedestrian into a whole. However, appearance features are always view-and illumination-sensitive. In this paper, we present a method to solve inter-camera pedestrian association via discriminative learned feature in a stable way with illumination transfer. First, we proposed a discriminative feature learning model which is a convolution siamese network that combines the verification and identification losses. Furthermore, we introduce color brightness transfer reduce color distortions under different illumination. To learn proper brightness transfer function, a fuzzy color cluster is used to model the change of color brightness between different cameras. The experiments show the effectiveness of the proposed method and achieve the state-of-the-art in the benchmark NLRP_MCT dataset.

关键词： Illumination transfer Inter-camera association A Discriminatively learning model Non-overlapping field of views

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An Improved Method of 3D Scene Reconstruction Based on SfM 3

An Improved Method of 3D Scene Reconstruction Based on SfM

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3rd international conference on robotics and automation Engineering (ICRAE)

作者： Yuan, Yating Ding, Yan Zhao, Liangjin Lv, Lin Beijing Inst Technol Sch Aerosp Engn Minist Educ Key Lab Dynam & Control Flight Vehicle Beijing 100081 Peoples R China

ISBN: (纸本)9781538695944

The 3D virtual scenes are widely used in different applications, such as visual simulation, augmented reality and virtual reality and so on. For reducing the time consumption with 3D modeling, we propose an improved method of 3D scene reconstruction based on SfM. By taking the video streaming as input, we put forward a feature similarity determination strategy to extract keyframes, and utilize a dense algorithm to improve the model accuracy. Moreover, the method appends 3D model filtering to remove the redundancy of the result models. We demonstrate our method by testing the time cost and effect of different videos and datasets. Results illustrate that our method is practical and available.

关键词： 3D scene reconstruction SfM keyframe extraction filtering

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The Design of Future Robotic Construction Lab

The Design of Future Robotic Construction Lab

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ASCE international conference on Computing in Civil Engineering (i3CE)

作者： Yang, C. H. Wu, T. H. Kang, S. C. Univ Alberta Dept Civil & Environm Engn 116 St & 85 Ave Edmonton AB T6G 2R3 Canada Natl Taiwan Univ Dept Civil Engn 1Sec 4Roosevelt Rd Taipei 10617 Taiwan

ISBN: (纸本)9780784482438

The increasing labor shortage issue and the working safety awareness cause the urgent of the development of new construction methods. A new type of lab for new construction processes is required to expedite the innovation cycle. This paper presents an ongoing work of building a construction lab at University of Alberta. The goal of the construction lab is to provide a sandbox for developing new construction processes and machines. We designed the lab with four major systems: (1) sensors: to collect data from construction site for operation assistants and virtual reconstruction;(2) manipulators: to excavate the path planning algorithms and to develop the cooperation approaches between human and machines;(3) visualizers: to construct the digital twin of a real construction site for revealing the simulated results in virtual environment;(4) computers: to run machine learning algorithms for recognizing and tracking objects in construction environments. This laboratory allows the researchers in the construction engineering test and develop their tools in a controlled environment. Such scaled tests in the lab can bring significant benefits in finance, efficiency, and safety. Technical Area: robotics, automation, and control. Application Context: Project design, construction, planning, and management.

关键词： automation control robotics

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Drift-Free Latent Space Representation for Soft Strain Sensors

Drift-Free Latent Space Representation for Soft Strain Senso...

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IEEE international conference on Soft robotics (RoboSoft)

作者： Thomas George Thuruthel Kieran Gilday Fumiya Iida Bio-Inspired Robotics Lab University of Cambridge UK

ISBN: (数字)9781728165707

ISBN: (纸本)9781728165714

Soft strain sensors are becoming increasingly popular for obtaining tactile information in soft robotic applications. Diverse technological solutions are being investigated to design these sensors. Simultaneously, new methods for modeling these sensor are being proposed due to their highly nonlinear, time varying properties. Among them, machine learning based approaches, particularly using dynamic recurrent neural networks look the most promising. However, these complex networks have large number of free parameters to be tuned, making it difficult to apply them for real-world applications. This paper introduces the concept of transfer learning for modelling soft strain sensors, which allows us to utilize information learned in one task to be applied to another task. We demonstrate this technique on a passive anthropomorphic finger with embedded strain sensors used for two regression tasks. We show how the transfer learning approach can drastically reduce the number of free parameters to be tuned for learning new skills. This work is an important step towards scaling of sensor networks (algorithm-wise) and for using soft sensor data for high-level control tasks.

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