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Design and 3D Motion Modeling of a 300-m Gliding Robotic Dolphin

IFAC-PapersOnLine 2017年第1期50卷 12685-12690页

作者： Yuan, Jun Wu, Zhengxing Yu, Junzhi Zhou, Chao Tan, Min University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper presents the mechatronic design and three-dimensional (3D) motion modeling of a gliding robotic dolphin capable of diving as deep as 300 m. Propulsion modes of bio-inspired robotic dolphin and traditional underwater gliders are integrated into the robot in order to endow it with both high maneuverability and long endurance simultaneously. It is realized by introducing a buoyancy adjusting mechanism on the basis of a bio-inspired robotic dolphin. Particular design on mechanical seal and compressive strength is implemented to achieve large diving depth. Further, a full-state dynamic model for 3D gliding motion is established for motion analysis. In comparison with traditional underwater gliders, the robot's flippers and fluke are controllable and thereby enrich the state-regulating modes of gliding motion, which is considered specially in the dynamic model. Simulations are conducted to analyze the robot's 3D gliding motion. Experiments are also carried out to validate the robot's gliding performance and the effectiveness of the formulated dynamic model. © 2017

关键词： 3D modeling Biomimetics Compressive strength Dolphins (structures) Dynamic models Dynamics Machine design Motion control robotics Robots Adjusting mechanism Bio inspired robotics Mechatronic design Robotic dolphin state regulating Three dimensional (3D) motion Underwater gliders Underwater robotics

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Calibration of atomic force microscope probes using a pneumatic micromanipulation system 1

Calibration of atomic force microscope probes using a pneuma...

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1st International Conference on Manipulation, Automation and robotics at Small Scales, MARSS 2017

作者： Zhang, Hao Hussain, Danish Meng, Xianghe Song, Jianmin Xie, Hui State Key Laboratory of Robotics and Systems Harbin Institute of Technology 2 Yikuang Street Nangang District Harbin150080 China

ISBN: (纸本)9781538603468

This paper presents an easy-to-use and efficient implementation of the added mass method for calibrating various atomic force microscope (AFM) probes without damage to the probe, which is based on the added mass method. The method is achieved by using a pneumatic control system and a home-made glass micro-pipette. Compared with the conventional added mass method, this method achieves the operation of the loading mass by negative pressure rather than the capillary force/electrostatic force, which is more stable and reliable. Therefore, it is efficient and reliable for placing/removing the loading mass to/from the AFM probe. It is a major significance for the calibration of unconventional shape cantilever. To confirm its ability, the polystyrene (PS) sphere is used as the loading mass to calibrate various AFM probes. The above experimental results show that this method can accurately and conveniently calibrate various AFM probes with less time consuming. © 2017 IEEE.

关键词： Calibration

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Feature-Based and Convolutional Neural Network Fusion Method for Visual Relocalization

Feature-Based and Convolutional Neural Network Fusion Method...

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International Conference on control, Automation, robotics and Vision

作者： Li Wang Ruifeng Li Jingwen Sun Hock Soon Seah Chee Kwang Quah Lijun Zhao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Nanyang Technological University Singapore ST Electronics (Training & Simulation Systems) Pte Ltd Singapore

Relocalization is one of the necessary modules for mobile robots in long-term autonomous movement in an environment. Currently, visual relocalization algorithms mainly include feature-based methods and CNN-based (Convolutional Neural Network) methods. Feature-based methods can achieve high localization accuracy in feature-rich scenes, but the error is quite large or it even fails in cases with motion blur, texture-less scene and changing view angle. CNN-based methods usually have better robustness but poor localization accuracy. For this reason, a visual relocalization algorithm that combines the advantages of the two methods is proposed in this paper. The BoVW (Bag of Visual Words) model is used to search for the most similar image in the training dataset. PnP (Perspective n Points) and RANSAC (Random Sample Consensus) are employed to estimate an initial pose. Then the number of inliers is utilized as a criterion whether the feature-based method or the CNN-based method is to be leveraged. Compared with a previous CNN-based method, PoseNet, the average position error is reduced by 45.6% and the average orientation error is reduced by 67.4% on Microsoft's 7-Scenes datasets, which verifies the effectiveness of the proposed algorithm.

关键词： Visualization Feature extraction Training Cameras Vocabulary Image retrieval Robots

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Robust ADRC for Nonlinear Time-varying System with Uncertainties 6

Robust ADRC for Nonlinear Time-varying System with Uncertain...

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2017 IEEE 6th Data Driven control and Learning systems Conference (DDCLS’17)

作者： Xiangyang Li Wei Ai Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology IEEE Center for Advanced Control Technologies Department of Electrical and Computer EngineeringCleveland State University

ISBN: (纸本)9781509054626

Active disturbance rejection control(ADRC) exemplifies the spirit of the data-driven control(DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertainties, without the premise that the detailed mathematical model of the controlled system is given. Instead, it is shown that the information needed for the control system to work at high level of effectiveness can be extracted from the input-output data by the use of the extended state observer(ESO). On the other hand, it is shown in this paper that the robustness of ADRC depends on the effectiveness of ESO. Furthermore, taking advantage of the rich body of knowledge in the existing field of robust control, the estimation error in ESO is analysed and, for the purpose of improved robustness, a unique nonlinear component is added to the conventional ADRC law. The modified ADRC which is a kind of robust ADRC law is validated in simulation for a nonlinear time-varying system with parametric and functional uncertainties. It is shown that the proposed robust ADRC law provides more effective tracking performance than the conventional ADRC when the bandwidth of ESO is not wide enough.

关键词： Active Disturbance Rejection control(ADRC) Robust control Extended state Observer(ESO) Nonlinear Time-varying System

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Parse geometry from a line: Monocular depth estimation with partial laser observation

Parse geometry from a line: Monocular depth estimation with ...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Liao, Yiyi Huang, Lichao Wang, Yue Kodagoda, Sarath Yu, Yinan Liu, Yong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China Horizon Robotics Inc. Beijing and Shenzhen China University of Technology Sydney Australia

ISBN: (纸本)9781509046331

Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increasing interest in depth estimation using monocular images. As this task is inherently ambiguous, the data-driven estimated depth might be unreliable in robotics applications. In this paper, we have attempted to improve the precision of monocular depth estimation by introducing 2D planar observation from the remaining laser range finder without extra cost. Specifically, we construct a dense reference map from the sparse laser range data, redefining the depth estimation task as estimating the distance between the real and the reference depth. To solve the problem, we construct a novel residual of residual neural network, and tightly combine the classification and regression losses for continuous depth estimation. Experimental results suggest that our method achieves considerable promotion compared to the state-of-the-art methods on both NYUD2 and KITTI, validating the effectiveness of our method on leveraging the additional sensory information. We further demonstrate the potential usage of our method in obstacle avoidance where our methodology provides comprehensive depth information compared to the solution using monocular camera or 2D laser range finder alone. © 2017 IEEE.

关键词： robotics

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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IEEE Intelligent Vehicles Symposium

作者： Jianliang Zhu Yunfeng Ai Bin Tian Dongpu Cao Sebastian Scherer School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Mechanical & Mechatronics Engineering University of Waterloo Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781538644539

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.

关键词： Hamming distance Intelligent vehicles binarization Recognition (Psychology) Visualization network model Cameras images

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Real-time 3D human tracking for mobile robots with multisensors

Real-time 3D human tracking for mobile robots with multisens...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Wang, Mengmeng Su, Daobilige Shi, Lei Liu, Yong Miro, Jaime Valls State Key Laboratory Institute of Cyber-Systems and Control Zhejiang University Zhejiang310027 China University of Technology Sydney Australia

ISBN: (纸本)9781509046331

Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system consists of three sub-modules: monocular camera sensor tracking model, ultrasonic sensor tracking model and multi-sensor fusion. An improved visual tracking algorithm is presented to provide partial location estimation (2-D). The algorithm is designed to overcome severe occlusions, scale variation, target missing and achieve robust re-detection. The scale accuracy is further enhanced by the estimated 3-D information. An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D). EKF is adopted to sequentially process multiple, heterogeneous measurements arriving in an asynchronous order from the vision sensor and the ultrasonic sensor separately. In the experiments, the proposed tracking system is tested in both simulation platform and actual mobile robot for various indoor and outdoor scenes. The experimental results show the superior performance of the 3-D tracking system in terms of both the accuracy and robustness. © 2017 IEEE.

关键词： Ultrasonic sensors

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Two-Layer Predictive control of the Copper Smelting Process Blowed by Enriched Oxygen at Bottom 7

Two-Layer Predictive Control of the Copper Smelting Process ...

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7th IEEE Annual International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2017

作者： Bin, Wang Zhuo, Wang Haibin, Yu Yang, Jia Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781538604892

Due to the nonlinearity, coupling, and delay during the copper smelting process blowed by enriched oxygen at bottom, a novel two-layer predictive control scheme is presented. The objective of the scheme is to minimize the cost of the material. The two-layer predictive control method achieves the steady-state optimal setpoints and resolves the multivariable dynamic control problems synchronously. Industrial test results show that the two-layer predictive control strategy leads to a significant improvement of control performance, as compared to manual controller within a multiloop framework. © 2017 IEEE.

关键词： Oxygen

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Zhu, Jianliang Ai, Yunfeng Tian, Bin Cao, Dongpu Scherer, Sebastian School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781538644522

关键词： Hamming distance

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Development of deep sea ARV cables physical characteristics

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IOP Conference Series: Earth and Environmental Science 2020年第1期461卷

作者： Chen Peng Yang Shangkui Cao Yongzhan Zhu Shuyun Liu Yuwang Ge Zhuang Bai Shihong School of Mechanical Engineering Shenyang Ligong University 110000 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences 110000 China School of Mechanical Engineering and Automation Northeastern University 110000 China Information and Control Engineering Faculty Shenyang Jianzhu University 110000 China School of Automation and Electrical Engineering Shenyang Ligong University 110000 China

Aiming at the challenging frontier problem of umbilical cable properties theory, which has been puzzled by this kind of robot for many years but has not been solved yet, this paper proposes an umbilical cable properties modelling method based on Kirchhoff elastic bar theory. This method analyzes the force on the umbilical cable member in the equilibrium state, obtains the Kirchhoff equation of the mobile cable in the equilibrium state, and then establishes the physical characteristics model of the mobile cable, and obtains the form of umbilical cable and the stress on the end point by solving the model. The new modelling theory research on the physical characteristics of deep-sea umbilical cable carried out in this paper is expected to solve the problem of 'unclear' problems of deep-sea slender umbilical cable, get rid of the dilemma of 'blind man and elephant', and provide guarantee for the safe and efficient operation of deep-sea robots.

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