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arXiv 2020年

作者： Wang, Fujun J. Cao, Zining Tan, Lixing Li, Zhen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Electro-optic Control Laboratory Luoyang China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

Probabilistic behavior is omnipresent in computer controlled systems, in particular, so-called safety-critical hybrid systems, because of various reasons, like uncertain environments, or fundamental properties of nature. In this paper, we extend existing hybrid process algebra ACPsrths with probability without replacing nondeterministic choice operator. In view of some shortcomings in existing approximate probabilistic bisimulation, we relax the constrains and propose a novel approximate probabilistic bisimulation relation. After that, we present a performance evaluation language CTRML, to reason over probabilistic systems, which extend the results to real number. Along with the specification language, we present a set of algorithms for the evaluation of the language. Additionally, we transfer the hybrid process algebra to probabilistic transition system and show experimental results. Copyright © 2020, The Authors. All rights reserved.

关键词： Hybrid systems

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A Human-Robot Skill Transfer Framework of Mobile Medical Robots for Autonomous Motion with Teaching by Demonstration

A Human-Robot Skill Transfer Framework of Mobile Medical Rob...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiehao Li Junzheng Wang Shoukun Wang Hui Peng Qi Wen Longbin Zhang Meina Lin Politecnico di Milano Milan Italy State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China College of Intelligence Science and Technology National University of Defense Technology Changsha China BioMEx Center KTH Mechanics KTH Royal Institute of Technology Stockholm Sweden Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

The accuracy of autonomous motion is the main challenge for mobile medical robots, especially running in narrow space conditions to transport the casualties in the hospital. This article mainly centers on the human-robot transmission of the mobile medical robot for narrow space autonomous motion. In order to enable mobile medical robots to autonomously and smoothly transport hospital casualties through narrow aisles, in this paper, we present a robot skill transfer technology, namely a human-robot skill transfer framework of mobile medical robots for autonomous motion with teaching by demonstration, which is that a smooth optimal path is planned through the human motion, and then the Kinect sensor is used to detect human bone movement to learn its motion trajectory so as to achieve trajectory tracking control. Meanwhile, using the Remote Center of Motion (RCM) constraint to minimize the error between the actual trajectory of the medical robot and the tracking reference path to control the motion trajectory accurately, human-computer interactive mobile medical robots can smoothly pass through narrow channels. Through research and analysis, it is demonstrated that the mobile medical robot proposed in this paper has great feasibility, which is of great reference value in the field of medical rescue.

关键词： Medical robotics Education Trajectory Mobile robots Tracking Hospitals

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Data-Driven Parametric Design of Additively Manufactured Hybrid Lattice Structure for Stiffness and Wide-Band Damping Performance

Additive Manufacturing Frontiers

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Additive Manufacturing Frontiers 2025年

作者： Chenyang Li Shangqin Yuan Han Zhang Shaoying Li Xinyue Li Jihong Zhu State IJR Center of Aerospace Design and Additive Manufacturing School of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China Key Laboratory of Metal High-Performance Additive Manufacturing and Innovative Design MIIT China Northwestern Polytechnical University Xi'an 710072 China Institute of Intelligence Material and Structure Unmanned System Research Institute Northwestern Polytechnical University Xi'an 710072 China Aircraft Strength Research Institute of China Xi'an 710065 China Modern Control Technology Research Institute Xi'an 710065 China

The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads. Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design. However, traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost. In this study, a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments. Subsequently, a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties. The response surface method was adopted to define the sensitive optimization target. A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network. Then, it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics. Validation experiments were conducted, demonstrating that the optimized hybrid lattice can achieve the target properties. In addition, the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.

关键词： Hybrid lattice structure Data-driven wide-band damping machine-learning method

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Learning Stable control for a Wheeled Inverted Pendulum with Fast Adaptive Neural Network

Learning Stable Control for a Wheeled Inverted Pendulum with...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yuanzhe Peng Yongsheng Ou Wei Feng Chinese Academy of Sciences (CAS) Shenzhen Institutes of Advanced Technology (SIAT) Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Since it is difficult to work out a complete and accurate analytical model for some complex nonlinear dynamical systems, we mainly propose a method for learning stable control with fast adaptive neural network. Furthermore, we provide a rigorous analytical evaluation of our proposed method in terms of stability. In order to validate the theoretical analysis, our proposed method has been applied in the stable control of a wheeled inverted pendulum (WIP) platform. It achieves high prediction accuracy and fast learning speed. In addition, the proposed method has incremental learning capabilities. When a new instance is provided, there is no need to retrain the entire training set, thus, the controller is more effective and more generalized.

关键词： Neural networks Adaptation models Mathematical model Analytical models Real-time systems Lead Nonlinear dynamical systems

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Relevance attack on detectors

arXiv

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arXiv 2020年

作者： Chen, Sizhe He, Fan Huang, Xiaolin Zhang, Kun Department of Automation The Institute of Medical Robotics The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Philosophy Department and Machine Learning Department Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States

This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the diversity across architectures. To pursue a high attack transferability, one plausible way is to find a common property across detectors, which facilitates the discovery of common weaknesses. We are the first to suggest that the relevance map from interpreters for detectors is such a property. Based on it, we design a Relevance Attack on Detectors (RAD), which achieves a state-of-the-art transferability, exceeding existing results by above 20%. On MS COCO, the detection mAPs for all 8 black-box architectures are more than halved and the segmentation mAPs are also significantly influenced. Given the great transferability of RAD, we generate the first adversarial dataset for object detection and instance segmentation, i.e., Adversarial Objects in COntext (AOCO), which helps to quickly evaluate and improve the robustness of detectors. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Visual SLAM based on geometric cluster matching

Visual SLAM based on geometric cluster matching

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Tian, Chaoran Ou, Yongsheng Qu, Yangyang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University OfChinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes OfAdvanced Technology Chinese Academy OfSciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728137261

The approach of feature-based visual SLAM is very popular in Micro Aerial Vehicle (MAV) navigation and wheeled mobile robot (WMR) navigation. Most modern feature-based visual SLAM frameworks extract a fixed number of feature points and then match the feature points by brute force or adopt Fast Library for Approximate Nearest Neighbors (FLANN). The features' extracting and matching processes will determine the localization accuracy and mapping quality significantly. In this paper, we propose a feature matching algorithm for the robot equipped with a binocular camera, which provides accurate real-time localization performance and robustness. In our feature matching method, both the geometric relationship of image feature points and the similarity of descriptor are considered. This paper presents a visual SLAM framework based on geometric cluster matching and the effectiveness of the proposed method is verified by practical experiments. © 2019 IEEE.

关键词： Mobile robots

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A scalable system to synthesize data for natural scene text localization and recognition

A scalable system to synthesize data for natural scene text ...

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Wu, Qingtian Chen, Peng Zhou, Yimin Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Engineering and Technology Research Center of Intelligent Unmanned System and Autonomous Environmental Perception China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology China School of Information Engineering Nanchang University 330038 China

ISBN: (纸本)9781728137261

In this paper, a scalable system is proposed to synthesize data for multilingual text (Chinese and English) detection and recognition in cluttered conditions. The system can be realized by these following steps. First, in order to respect scene boundaries when overlaying synthetic text to existing images, hierarchical contour is obtained by using efficient graph-segmentation. Second, in order to paint text on top of surfaces with approximate natural effect, scene 3D geometry is estimated under a single image by using Convolutional Neural Network (CNN) based processing. Finally, in order to synthesize data more naturally, the text is printed into the picture appropriately by poisson image editing and the text color is rendered by a combination of foreground and background colors. The system can generate scene text data automatically and naturally, providing a cheap way to annotate massive images to train supervisor learning model for natural scene text detection and recognition. © 2018 IEEE

关键词： Image segmentation

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Binocular depth estimation using convolutional neural network with siamese branches

Binocular depth estimation using convolutional neural networ...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. The first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

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An Obstacles Avoidance Algorithm Based on Improved Artificial Potential Field

An Obstacles Avoidance Algorithm Based on Improved Artificia...

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IEEE International Conference on Mechatronics and Automation

作者： Si Xi Lu En Li Rui Guo School of Artificial Intelligence University of Chinese Academy of Sciences No.19(A) Yuquan Road Beijing China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science 95 East ZhongGuanCun Road Beijing China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Obstacle avoidance is one of the most important issue in the motion planning and control of robot. There are many algorithms to avoid obstacles, but obstacle avoidance algorithm for manipulator is needed consider not only end effector, but also all links of manipulator. In terms of real-time and speed of all system, the algorithm also should be easy as possible. In this paper, an algorithm based on the improved artificial potential field is proposed to solve the problem of the local optimum. The idea of improvement is to find a path by fitting curve with target point and critical oscillating points. There is also a comparison in this paper with planning a path by tangent line. The conclusion proves that the path is much smoother by the algorithm based on improved artificial potential field in this paper. Furthermore, the problem of judder on the trajectory of traditional artificial potential field is also solved in this paper by an improved repulsive potential field function. Finally, the algorithm is proved by planning a smooth path for obstacle avoidance on manipulator: KinovaGen3 in MATLAB.

关键词： Mechatronics Fitting End effectors Real-time systems Planning Trajectory Collision avoidance

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