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Hovering control of an Underwater Vehicle

Hovering Control of an Underwater Vehicle

引用

2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Cao, Yaozhong Song, Dalei Wang, Zhan Wang, Yu University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Ocean University of China Department of Automation and Measurement Qingdao266100 China Zhejiang Provincial Energy Group Company Ltd Hangzhou310000 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

This paper aims to achieve the hovering control of an underwater vehicle with uncertain parameters based on cascade PID. First, the inner loops for each channels are designed respectively. Then, the outer loops including depth controller, horizontal position controller and yaw controller are designed. The vector transformation method are used to force the vehicle to approach the target point along approximately straight line in the horizontal plane. Simulation results demonstrate the stability and validity of the proposed method. © 2022 IEEE.

关键词： controllers

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A Convolutional Neural Network With Multi-scale Kernel and Feature Fusion for sEMG-based Gesture Recognition

A Convolutional Neural Network With Multi-scale Kernel and F...

引用

IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO)

作者： Han, Lijun Zou, Yongxiang Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

ISBN: (纸本)9781665405355

The sEMG-based gesture recognition has great potential in human-computer interaction. However, the current approaches are far from optimal. In this paper, a novel convolutional neural network which combines the multi-scale kernal and feature fusion (MKFF-CNN) is proposed. This model could extract multi-scale features and make full use of these feature maps. One dataset called "gForce dataset" is recorded in this work, comprising 13 able-bodied participants. To verify the effectiveness of the proposed model, evaluations on both the gForce dataset and the Ninapro DB6 are conducted. The experimental results demonstrate that, MKFF-CNN achieves an accuracy of 97.65% on the gForce dataset, which is 1.39% higher than that of the subnetwork without early fusion, and at least 6.54% higher than that of the subnetwork with single-scale convolution kernel. On the Ninapro DB6, MKFF-CNN achieves 98.52% accuracy, which is 1.3% higher than the state-of-the-art works, showing the superiority of the proposed model.

关键词： Surface electromyography hand gesture recognition convolutional neural network multi-scale kernel feature fusion

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Learning Pre-grasp Pushing Manipulation of Wide and Flat Objects Using Binary Masks 28th

Learning Pre-grasp Pushing Manipulation of Wide and Flat Obj...

引用

28th International Conference on Neural Information Processing

作者： Wu, Jiaxi Zhong, Shanlin Li, Yinlin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9783030922726;9783030922733

For wide and flat objects, such as books and package boxes, the robot arm cannot grasp directly because of the limit of the gripper width. Therefore, pre-grasp manipulation is needed to make them gras-pable, e.g., pushing it to the table side and grasp from the side. In this paper, we propose a novel method to learn the pre-grasp manipulation based on deep reinforcement learning. Instead of directly feeding the raw images, we generate the binary masks as states according to the shape and current position of the object and table. Meanwhile, we use adaptive resets to accelerate the training process, i.e., adaptively reset the initial states to demonstration states. After training on a large scale of automatically generated objects and tables of different shapes, our proposed method achieves a 97.6% success rate on novel objects. The detailed experimental videos are available at https://***/view/pre-grasp.

关键词： Pre-grasp manipulation Deep reinforcement learning Adaptive resets

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Dynamic Modeling and Optimization of Robotic Fish Based on Passive Flexible Mechanism 18

Dynamic Modeling and Optimization of Robotic Fish Based on P...

引用

IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Zou, Qianqian Lu, Ben Fu, Yuzhuo Liao, Xiaocun Zhang, Zhuoliang Zhou, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781665441018

This paper proposes a modeling method for a robotic fish with elastic tail, which is driven by a motor in head and uses a spring steel sheet as a passive flexible joint, followed by a rigid caudal fin. The pseudo-rigid-body model (PRBM) is firstly used to analyze the flexible joint, then the fluid force acting on the robotic fish is divided into the pressure on links and cross-sectional resistance. Further, a dynamic model is established by the Lagrangian dynamic method to solve the movement of the robotic fish. And the model is utilized to investigate the influence of joint stiffness on propulsion performance. For different frequencies, the optimal joint stiffness is calculated to maximize the swimming speed. These optimized speeds are all higher than using rigid joints, and a maximum speed increase of 033 body length per second is reached at the largest simulation frequency 10Hz. In addition, the swimming speed can be maximized at the same input power by adjusting the stiffness of the joint. These findings prove the effectiveness of passive flexible joints in optimizing the performance of the robot fish.

关键词： robotic fish dynamic modeling flexible joint

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Analysis of the Total Orientation Workspace of a Type of n-PPPS Parallel Manipulator 6

Analysis of the Total Orientation Workspace of a Type of n-P...

引用

6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Liu, Zhaoyang Fan, Junfeng Wang, Zhe Jing, Fengshui Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9780738133645

The total orientation workspace (TOW) is fundamental to the design and application of a parallel manipulator. Among various PM structures, a type of n-PPPS PMs, whose limbs consisted of three active prismatic joints and one passive spherical joint, is widely used in assembly tasks of jointing cabin sections. In this paper, the shape of the TOW of the type of n-PPPS PM is proved to be a cuboid. And this property is utilized to transfer the TOW determination problem to solving six minima and maxima with the help of nonlinear optimization algorithm, which reduces the computation cost greatly comparing to general workspace determination algorithms. Then, a 4-PPPS PM is taken as an example to carry out comparisons on computation speed and accuracy with a general TOW solving method based on workspace search, and the experiment results show the superiority of the proposed method. In the end, the influence of limb distances and angle ranges of the TOW on the volume rate of the TOW is analyzed as a concrete example of the application of the proposed method to guide manipulator design and application.

关键词： Mechatronics Costs Shape Conferences Manipulators Task analysis Optimization

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Semantic Attention-Based Network for Inshore SAR Ship Detection 13

Semantic Attention-Based Network for Inshore SAR Ship Detect...

引用

13th International Conference on Digital Image Processing (ICDIP)

作者： Sun, Wenhao Huang, Xiayuan Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

ISBN: (数字)9781510646018

ISBN: (纸本)9781510646018

The performance of Synthetic Aperture Radar (SAR) ship detector has been significantly improved with the development of convolutional neural network. However, the issue of effective detection of inshore ships is still a challenging problem. In this paper, we propose a novel one-stage SAR ship detector, called Semantic Attention-Based Network (SANet), which can largely improve the accuracy of ship detection in the inshore scenario without compromising the speed. Specifically, we introduce a semantic attention mechanism, which will highlight the features from the ships area and enhance the detector's classification ability. We train the proposed Semantic Attention Module with focal loss, and assign labels for the attention maps by center sampling. Combined with our anchor assign strategy, our SANet achieves state-of-the-art results on the open SAR Ship Detection Dataset (SSDD).

关键词： Semantic Attention-Based Network (SANet) SAR images ship detection inshore scenario

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A Combined Indoor Self-positioning Method for Robotic Fish Based on Multi-sensor Fusion 18

A Combined Indoor Self-positioning Method for Robotic Fish B...

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IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Fu, Yuzhuo Lu, Ben Liao, Xiaocun Zou, Qianqian Zhang, Zhuoliang Zhou, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781665441018

In an experimental environment with limited conditions, it is always hard to achieve precise positioning of robotic fish. A combined indoor self-positioning method in this paper is introduced to solve the problem. For the short-distance range, coordinates are calculated by fusing the measured distances and angles. For the medium-distance range, a clustering-grid supervision (CGS) algorithm is proposed and adopted to correct the coordinates obtained by the four-point positioning method. An ostracion-like robotic fish is used as the experimental object to achieve centimeter-level positioning with an average positioning error of 4.492 cm in a short-distance range and decimeter-level positioning with an error of 2.049 dm in a medium-distance range. Compared with traditional methods, this comprehensive method has the advantages of low cost and high accuracy.

关键词： multi-sensor fusion robotic fish indoor positioning

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Learning from Crowd labeling with Semi-crowdsourced Deep Generative Models 15th

Learning from Crowd Labeling with Semi-crowdsourced Deep Gen...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Wei, Xuan Zhang, Mingyue Zeng, Daniel Dajun Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China School of Business and Management Shanghai International Studies University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Shenzhen China

ISBN: (纸本)9789811625398

Microtask crowdsourcing has become an appealing approach to collecting large-scale high-quality labeled data across a wide range of domains. As the crowd workers may be unreliable, the most fundamental question is how to aggregate the noisy annotations provided by these potentially unreliable workers. Although various factors such as worker reliability and task features are considered in the literature, they are not meaningfully combined in a unified and consistent framework. In this work, we propose a semi-crowdsourced deep generative approach called S-DARFC which combines Bayesian graphical models and deep learning techniques and unifies factors including the worker reliability, task features, task clustering structure, and semi-crowdsourcing. Graphical models are good at finding a structure that is interpretable and generalizes to new tasks easily and deep learning techniques are able to learn a flexible representation of complex high-dimensional unstructured data (e.g., task features). Extensive experiments based on six real-world tasks including text and image classification demonstrate the effectiveness of our proposed approach. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crowdsourcing

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An Elastic Biomimetic Fish Tail and Its Undulation Fitting Method of Body Wave 18

An Elastic Biomimetic Fish Tail and Its Undulation Fitting M...

引用

IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Liao, Xiaocun Fu, Yuzhuo Lu, Ben Zou, Qianqian Zhang, Zhuoliang Zhou, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781665441018

This paper presents a novel wire-driven fish tall with two joints, using spring-steel-sheet to mimic fish spine, which makes the whole fish tail compliant and can simulate real fish better. This mechanism can also achieve C-shape and S-shape swing similar to natural fish tail. To predict the attitude of fish tail, kinematics model is established theoretically in this paper. The simulation experiments show that the maximum error between the amplitude predicted by the kinematics model and expected amplitude at the end of the fish tail is 5 mm, indicating the correctness of the proposed kinematics model. In order to solve the control coupling problem of servomotors, this paper further proposes a based-vision biomimetic undulation fitting method, which is both simple and efficient. Finally, the proposed method is verified by two groups of experiments involving forward motion and turn motion. Experimental results are in good agreement with the expected values. When the frequency is 0.1 Hz, the average errors of forward motion and turn motion are 0.59 degrees and 0.7 degrees respectively, which proves that the proposed method is reliable and effective.

关键词： Elastic biomimetic fish tail Kinematics model control coupling Undulation fitting method

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Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial Muscles 13

Adaptive Takagi-Sugeno Fuzzy Model for Pneumatic Artificial ...

引用

13th International Conference on Advanced Computational Intelligence (ICACI)

作者： Xia, Xiuze Cheng, Long Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781665412544

Pneumatic artificial muscle (PAM) usually exhibits strong hysteresis nonlinearity and time-varying features that bring PAM modeling and control difficulties. In this paper, an adaptive Takagi-Sugeno (T-S) fuzzy model is established based on nonlinear auto-regression moving average with exogenous input (NARMAX) structure to describe PAM's characteristics. Experiments show that compared with other phenomenology-based models, the presented model has lower predictive error and better adaptability. Finally, a model predictive controller is designed and validated to verify the adaptive T-S fuzzy model's practicability.

关键词： Pneumatic artificial muscle NARMAX model adaptive T-S fuzzy model model predictive control

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