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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Liu, Xiaoming Kojima, Masaru Shi, Qing Wang, Huaping Sun, Tao Mae, Yasushi Huang, Qiang Arai, Tatsuo Fukuda, Toshio Intelligent Robotics Institute School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China Department of Systems Innovation Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan Faculty of Science and Engineering Meijo University 1-501 Shiogamaguchi Tenpaku-ku Nagoya468-8502 Japan

ISBN: (纸本)9781509046331

In micromanipulation, lots of methods have been developed to manipulate objects in microscale. However, few of them can be applied in both the transportation and the rotation of the micro objects. In this paper, we present a novel method to realize the non-contact transportation and rotation of the micro objects based on the vibration-induced swirl flow. A piezo actuator is set between the glass needle and a metal rod. The sine wave with controlled frequency and amplitude is input into the piezo actuator to drive the glass needle to move circularly, which is caused by resonance of the actuator and the metal rod. We place the glass needle under water and keep a limited distance to the bottom. The circular vibration of the glass needle can generate a swirl flow and low pressure around it. The low pressure can trap and transport the micro objects vertically to the glass needle, and the swirl flow can rotate the objects continuously. Finally, we realize the trap and rotation of micro object with only one piezo actuator. Experiments of transportation and rotation of microbeads are carried out, and the results demonstrate it is a simple, low-cost, effective micromanipulation method. © 2017 IEEE.

关键词： Needles

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

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

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

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

ISBN: (纸本)9798350331820

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. © 2023 IEEE.

关键词： Forecasting

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Design and simulation of a hip exoskeleton for lateral walking

Design and simulation of a hip exoskeleton for lateral walki...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zhang, Zhewen Cao, Wujing Chen, Chunjie Yu, Hongliu Wu, Xinyu Meng, Qiaoling Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology

ISBN: (纸本)9781665436786

In this article, a rehabilitation exoskeleton assisted by lateral walking is designed to help patients with lower limb movement disorders to solve the problem of lateral walking assistance, so as to achieve the effect of rehabilitation training. Based on the characteristics of lateral movement of human body, the exoskeleton designed in this paper uses linear push rod motor and thigh binding to assist hip abduction and adduction respectively. Based on the research of human transverse movement gait, a reasonable linkage mechanism was designed and kinematics simulation was carried out. The dynamics model of human lower limbs was established and calculated with MATLAB simulation software. the moment of hip joint lateral movement is obtained. Finally, the structural parameters of the driving mechanism were analyzed by dynamic simulation, and the simulation results were compared with the target motion function to verify the rationality of the design. © 2021 IEEE.

关键词： MATLAB

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Dynamical Balance Optimization and Control of Quadruped Robots Under Perturbing External Force

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IFAC Proceedings Volumes 2013年第20期46卷 199-205页

作者： Zhijun Li Sijia Yu Sibang Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education College of Automation Science and Engineering South China University of Technology Guangzhou China The Robotics Institute and School of Mechanical Engineering University of Electronic Science and Technology of China Chengdu China 610054 The Robotics Institute and Institute of Intelligent Systems and Information Technology University of Electronic Science and Technology of China Chengdu China 610054

This paper investigates the optimal feet forces distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamic model of quadruped robots and derive a reduced order dynamical model of motion/force. Consider an external wrench on quadruped robots, the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to minimize the optimized objective function with linear equality and inequality constraints, and hybrid motion/force control based on adaptive neural network is used to compensate for the external perturbation in the environment and approximate feedforward force and impedance of the leg joints on line. The verification of the proposed control is conducted using the extensive simulations.

关键词： quadruped robot forces distribution external wrench motion/force control

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Single image dehazing via combining the prior knowledge and CNNs

Single image dehazing via combining the prior knowledge and ...

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第33届中国控制与决策会议

作者： Yuwen Li Chaobing Zheng Shiqian Wu Wangming Xu Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology The Institute of Robotics and Intelligent Systems School of Information Science and EngineeringWuhan University of Science and Technology

Aiming at the existing single image haze removal algorithms,which are based on prior knowledge and assumptions,subject to many limitations in practical applications,and could suffer from noise and halo *** end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning *** haze image is decomposed into the base layer and detail layers through a weighted guided image filter(WGIF) firstly,and the airlight is estimated from the base ***,the base layer image is passed to the efficient deep convolutional network for estimating the transmission *** restore object close to the camera completely without amplifying noise in sky or heavily hazy scene,an adaptive strategy is proposed based on the value of the transmission *** the transmission map of a pixel is small,the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model,***,the haze image is *** show that the proposed method achieves superior performance over existing methods.

关键词： single image haze removal Weighted guided image filtering prior knowledge Convolutional Neural Networks

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Deposition of carbon nanoparticles using optically induced dielectrophoretic force

Deposition of carbon nanoparticles using optically induced d...

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International Solid-State Sensors, Actuators and Microsystems Conference

作者： Wang, S.E. Dong, Z.L. Qu, Y.L. Lee, G.B. Li, W.J. State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang China Graduate University Chinese Academy of Sciences China Department of Engineering Science National Cheng Kung University Taiwan Centre for Micro and Nano Systems Chinese Univerisity of Hong Kong Hong Kong

ISBN: (纸本)9781457701573

A new method which allows assembly of carbon nanoparticles (CNPs) into predesigned patterns by using optically induced dielectrophoretic (ODEP) force is presented in this study. Theoretical analysis of ODEP working principle and operational regimes are investigated. Moreover, experimental parameters including AC frequency and solution conductivity are optimized for manipulating micro/nano particles. Finally, experimental results clearly demonstrate that CNP patterns and arrays with arbitrary geometry (including a 16×16 array covering an area of 0.4×0.4 mm2) can be successfully realized. Our results proved that ODEP is a highly-effective, low-cost, reconfigurable, and parallel manipulating approach for potential applications in fabricating CNPs sensors and micro-printing. © 2011 IEEE.

关键词： Nanoparticles

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Lunar Asset Messaging and on Orbit Navigation (LA MOON)

Lunar Asset Messaging and on Orbit Navigation (LA MOON)

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IAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Derewa, Chrishma Holt, Samuel Lally, Michael Trybula, Robert Yuan, Christine Coble, Kyle Huang, Calvin Jet Propulsion Laboratory California Institute of Technology United States University of Colorado Boulder Aerospace Engineering United States New York University Computer Science United States University of Southern California Computer Science United States Cornell University Mechanical Engineering United States Kth Royal Institute of Technology Systems Control and Robotics Pasadena City College Computer Science United States

ISBN: (纸本)9781713842965

NASA has titled its 2020 thrust for the Moon, Artemis. The increased focus on the Moon as a destination for future human and robotic expeditions necessitates general purpose navigational and communications infrastructure reducing their complexity to help establish a sustained presence. A framework through which Lunar missions can relay communications and localize their positions shifts the burden from the individual mission and enables resource allocation tailored to mission-specific goals. During the summer of 2020, student interns under the Innovation to Flight (i2F) program at the National Aeronautics and Space Administration s (NASA) Jet Propulsion laboratory (JPL) in collaboration with the University of Colorado Boulder designed, built, and tested a prototype framework capable of providing surface assets with communication and positioning services. The team utilized the existing i2F CubeSat bus in addition to developing several CubeSat engineering development units (EDUs), a ground vehicle, and a ground station to simulate a scenario in which a lunar surface mission is supported by these services. A primary goal of the summer was to develop a method for localizing the ground vehicle through trilateration. Distances are inferred from the round-Trip time of flight (ToF) of radio signals between an asset and several elements. Signals were sent and received using LimeSDR software defined radios on-board both the ground vehicle and the EDUs;ToF and trilateration were calculated on a Qualcomm Snapdragon development board located within the LA MOON payload data system. The ModalAI chipset on the Qualcomm was instrumental in executing visual based position estimation. Communications was facilitated through a bent-pipe approach addressing the NASA requirement to provide solutions for in communication denied locations. The ground vehicle relayed information to other surface assets in addition to its ground station through the supporting constellation. This project demons

关键词： Orbits

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Dynamic fuzzy control and system stability for the Acrobot

Dynamic fuzzy control and system stability for the Acrobot

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： M.H. Smith T. Zhang W.A. Gruver Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Intelligent Robotics and Manufacturing Systems Laboratory Manufacturing Systems Laboratory Simon Fraser University Burnaby Canada

This paper discusses the swing-up control of a two link robot mechanism known as the Acrobot. Previously, a technique using meta-rules was proposed to compensate for external disturbances when using a dynamic fuzzy controller. In this paper, stability of the meta-rule system is analyzed from a global asymptotic stability viewpoint based on fuzzy characteristic matrices. These matrices are generated from the first phase of a three phase learning process involving the combination of genetic algorithms, dynamic switching fuzzy systems, and meta-rule techniques with the purpose of developing a better performing fuzzy controller.

关键词： Fuzzy control Stability Fuzzy systems Automatic control Fuzzy reasoning Robot kinematics Control systems Leg PD control Genetic algorithms

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Physics-Informed Neural Network for Multirotor Slung Load systems Modeling

Physics-Informed Neural Network for Multirotor Slung Load Sy...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Gil Serrano Marcelo Jacinto José Ribeiro-Gomes João Pinto Bruno J. Guerreiro Alexandre Bernardino Rita Cunha Institute for Systems and Robotics (ISR) Laboratory of Robotics and Engineering Systems (LARSyS) Instituto Superior Técnico University of Lisbon Portugal CTS/Uninova NOVA School of Science and Technology (FCT-NOVA) Caparica Portugal

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent advances in aerial robotics have enabled the use of multirotor vehicles for autonomous payload transportation. Resorting only to classical methods to reliably model a quadrotor carrying a cable-slung load poses significant challenges. On the other hand, purely data-driven learning methods do not comply by design with the problem’s physical constraints, especially in states that are not densely represented in training data. In this work, we explore the use of physics-informed neural networks to learn an end-to-end model of the multirotor-slung-load system and, at a given time, estimate a sequence of the future system states. An LSTM encoder-decoder with an attention mechanism is used to capture the dynamics of the system. To guarantee the cohesiveness between the multiple predicted states of the system, we propose the use of a physics-based term in the loss function, which includes a discretized physical model derived from first principles together with slack variables that allow for a small mismatch between expected and predicted values. To train the model, a dataset using a real-world quadrotor carrying a slung load was curated and is made available. Prediction results are presented and corroborate the feasibility of the approach. The proposed method outperforms both the first principles physical model and a comparable neural network model trained without the physics regularization proposed.

关键词： Training Neural networks Transportation Training data Predictive models systems modeling Vehicle dynamics

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Achievability for telerobotic systems

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AIP Conference Proceedings 2001年第1期552卷 52-57页

作者： Reid L. Kress John V. Draper William R. Hamel 1Mechanical and Aerospace Engineering and Engineering Science Department The University of Tennessee Knoxville Tennessee 2Robotics & Process Systems Division Oak Ridge National Laboratory Oak Ridge Tennessee

Methods are needed to improve the capabilities of autonomous robots to perform tasks that are difficult for contemporary robots, and to identify those tasks that robots cannot perform. Additionally, in the realm of remote handling, methods are needed to assess which tasks and/or subtasks are candidates for automation. We are developing a new approach to understanding the capability of autonomous robotic systems. This approach uses formalized methods for determining the achievability of tasks for robots, that is, the likelihood that an autonomous robot or telerobot can successfully complete a particular task. Any autonomous system may be represented in achievability space by the volume describing that system’s capabilities within the 3-axis space delineated by perception, cognition, and action. This volume may be thought of as a probability density with achievability decreasing as the distance from the centroid of the volume increases. Similarly, any task may be represented within achievability space. However, as tasks have more finite requirements for perception, cognition, and action, each may be represented as a point (or, more accurately, as a small sphere) within achievability space. Analysis of achievability can serve to identify, a priori, the survivability of robotic systems and the likelihood of mission success; it can be used to plan a mission or portions of a mission; it can be used to modify a mission plan to accommodate unpredicted occurrences; it can also serve to identify needs for modifications to robotic systems or tasks to improve achievability.

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