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Advanced Robot Programming: a Review

Current robotics Reports 2020年第4期1卷 251-258页

作者： Zhou, Zhongxiang Xiong, Rong Wang, Yue Zhang, Jiafan State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control of Zhejiang Province Zhejiang University Hangzhou China Research Center China Robotics and Discrete Automation ABB Shanghai China

The review presents an overview of advanced robot programming approaches which aims to ease robot programming and speed up the deployment of industrial robots, and then some considerations are shared with respect to requirements in new trends of manufacturing. The new trend of customization along with Industry 4.0 is appearing which is a challenge for robotized systems. The bottleneck is mainly the efficiency of deployment of industrial robots because traditional programming methods are not intuitive and always time-consuming. Advanced robot programming techniques are expected to ease robot programming and make it accessible for non-experts. A review on advanced robot programming is here presented, firstly introducing the background of this research, followed by reviewing literatures in four categories: programming by demonstration for low-level motion, programming by demonstration for high-level task, speech recognition-based and augmented reality-based programming approaches, and finishing on discussing future works.

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Reinforcement Learning with Large Language Model for Hybrid Disassembly Lines in Remanufacturing Contexts

Reinforcement Learning with Large Language Model for Hybrid ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Peng Ji Xi Wang Guo Jiacun Wang Weitian Wang Shu Jin Qin Ying Tang Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Fushun China Dept of Comp. Sci. and Soft. Eng. Monmouth University W. Long Branch NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Large language models (LLM), ChatGPT is making substantial impact across various fields. This study for the first time presents a novel approach to the hybrid disassembly line balancing problem using LLM and reinforcement learning algorithms in remanufacturing contexts. The problem is divided into two sub-stages. LLM is innovatively used to capture a disassembly sequence well in the first stage, while reinforcement learning is utilized to address the problem in the second stage. Upon comparing the performance with and without LLM, the proposed approach significantly reduces the trial-and-error space and achieves faster convergence to achieve the desired solution. Future work of this study is also discussed.

关键词： Computer aided software engineering Automation Large language models Reinforcement learning Chatbots Convergence

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Practical fractional-order variable-gain super-twisting control with application to wafer stages of photolithography systems

arXiv

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arXiv 2021年

作者： Kuang, Zhian Sun, Liting Gao, Huijun Tomizuka, Masayoshi Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Mechanical Control System Lab Mechanical Engineering Department University of California BerkeleyCA94720 United States

In this paper, a practical fractional-order variable-gain super-twisting algorithm (PFVSTA) is proposed to improve the tracking performance of wafer stages for semiconductor manufacturing. Based on the sliding mode control (SMC), the proposed PFVSTA enhances the tracking performance from three aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm and a novel fractional-order sliding surface (FSS) design, 2) improving the dynamics of states on the sliding surface with fast response and small overshoots via the designed novel FSS and 3) compensating for disturbances via variable-gain control law. Based on practical conditions, this paper analyzes the stability of the controller and illustrates the theoretical principle to compensate for the uncertainties caused by accelerations. Moreover, numerical simulations prove the effectiveness of the proposed sliding surface and control scheme, and they are in agreement with the theoretical analysis. Finally, practice-based comparative experiments are conducted. The results show that the proposed PFVSTA can achieve much better tracking performance than the conventional methods from various perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Sliding mode control

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Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection

Residual Squeeze-and-Excitation Network with Multi-scale Spa...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hu Cao Guang Chen Zhijun Li Jianjie Lin Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Technische Universität München München Germany State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Tongji University Shanghai China University of Science and Technology of China China

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

关键词： Automation Conferences Pose estimation Grasping Manipulators Feature extraction Inference algorithms

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Indoor Layout Estimation by Fusing Monocular RGB Image Features Extracted with HRNet

Indoor Layout Estimation by Fusing Monocular RGB Image Featu...

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第三十九届中国控制会议

作者： Rong-Ze Huang Yin-Bo Liu Meh Jabeen Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information EngineeringTianjin University

Indoor layout estimation(ILE) is a challenging research that segments the natural indoor scene image into floor,walls and ceiling,and it has a wide range of application prospects in the fields such as scene understanding,reconstruction,robot positioning,and virtual *** ILE method by merging multiple feature s from a monocular red-green-blue(RGB) image was ***,the High-resolution networks(HRNet) was used to extract the features of indoor corner keypoints(CKs) and informative edges(IEs) in the form of ***,the extracted features were then selected through a room type(RT)classification ***,a feature fusion algorithm was proposed,which obtained the initial layout hypothe sis through CK features and then continuously optimized the layout hypothe sis with IE *** proposed ILE method was validated on the large scene understanding challenge(LSUN) dataset.

关键词： Indoor layout estimation Corner keypoint Informative edge Fusion algorithm High-resolution net

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Odor Evaluation of Vehicle Interior Materials Based on Portable E-nose

Odor Evaluation of Vehicle Interior Materials Based on Porta...

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第三十九届中国控制会议

作者： Zhe-Hua Sun Ke-Xian Liu Xue-Han Xu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

The evaluation of the odor emitted by the interior materials is of great significance to the air quality control in the vehicle. This paper focuses on the multi-label classification problem of the odor type(i.e. the type of material from which the odor is released) and the grade evaluation in the vehicle. Two multi-label classifiers based on binary relevance(BR) and classification chain(CC) were designed to distinguish odor types and grades simultaneously and to distinguish odor types first and then grades, respectively. Based on the self-designed portable E-nose, six grades of odors released from six types of common interior materials were detected and classified. Experimental results showed that both the multi-label classifiers achieved better recognition accuracy, but the CC had an average recognition rate of 88.17% slightly better than the BR. It is preliminarily verified that the portable E-nose based multi-label classifiers could be used to evaluate the odor of automotive interior materials.

关键词： E-nose vehicle interior materials multi-label classifier Odor evaluation

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A Novel E-Nose Chamber Design for VOCs Detection in Automobiles

A Novel E-Nose Chamber Design for VOCs Detection in Automobi...

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第三十九届中国控制会议

作者： Lu Cheng Yin-Bo Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information EngineeringTianjin University

To detect parts per billion（ppb）-level volatile organic compounds（VOCs） in automobiles, a pre-concentrator is generally needed for electronic noses（e-noses）, which can enrich ppb-level concentration to parts per million（ppm） level. However, the current e-nose chamber structure could dilute the pre-concentrated gases and then the detection performance is reduced. To solve this problem, a novel chamber design was proposed in this paper. The core design idea was to reduce the space for the gas flow in the chamber, taking into account the shape and size of the gas sensors. Specially, for the pin-type gas sensors, a series of cylindrical structure design was adopted, and for the micro-electro-mechanical system（MEMS） patch-type sensors, the headspace was compressed. The results of computational fluid dynamics（CFD） based simulation showed that the concentration of pre-concentrated gases in the novel chamber was higher than that of the traditional chamber. The designed chambers were manufactured using polylactic acid（PLA） material by 3D printing. Experimental gases were prepared from polyurethane（PU） leather and polyvinyl chloride（PVC） leather to verify the performance of the chambers, and the results demonstrated that the proposed new e-nose chamber was superior to the traditional one in terms of the pre-concentration effect, repeatability and classification performance.

关键词： e-nose chamber pre-concentrator parts per billion（ppb） volatile organic compounds（VOCs） repeatability classification

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Monocular Visual SLAM based on VGG Feature Point Extraction

Monocular Visual SLAM based on VGG Feature Point Extraction

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第三十九届中国控制会议

作者： Xu-Yang Dai Qing-Hao Meng Wen-Jian Zheng Shao-Kai Zhu Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information Engineering Tianjin University

Significant progress has been made in the field of visual SLAM（Simultaneous Localization and Mapping）. However, the localization accuracy of visual SLAM can be significantly reduced in low-texture and illumination-changing environments. To solve these problems, an enhanced visual SLAM algorithm based on VGG（Visual Geometry Group） network was proposed in this paper. Firstly, the VGG network for feature point extraction was incorporated into the visual odometry（VO） to achieve robust camera pose estimation. Secondly, an automatic corner annotation method was adopted to set up the training database, which could reduce the workload of data annotation. Thirdly, to make the backend optimization more suitable for the VGG based VO, the BA（bundle adjustment） optimization process was improved. Experimental results showed that, the proposed visual SLAM method outperformed the mainstream ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM） method, in terms of the number of effective feature points, the robustness of feature matching process to light changes, and the accuracy of robot pose estimation.

关键词： monocular visual SLAM feature point extraction convolutional neural network

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An Easy-implemented Optimization Method of Trajectory Planning Based on Polynomial Interpolation

An Easy-implemented Optimization Method of Trajectory Planni...

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第40届中国控制会议

作者： Guangrong Chen Ming Ma Sheng Guo Bowen Hou Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Firstly,the forward kinematics and inverse kinematics of a five axis manipulator are ***,an easyimplemented optimization method of trajectory planning based on seventh order polynomial interpolation is *** proposed optimization method can capture the time optimal trajectory,while the actuators specifications including speed,acceleration of motor can be guaranteed as ***,simulation results validate the effectiveness and efficiency of proposed optimization *** research provides an insight for the application of trajectory optimization on the controller with low computing capability and real-time requirement.

关键词： Trajectory planning Optimization method Polynomial interpolation Manipulator

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Attitude Adjustment of a Multi-joint AUV Using an Improved LADRC

Attitude Adjustment of a Multi-joint AUV Using an Improved L...

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第三十九届中国控制会议

作者： Lin Yu Ke-Xian Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information Engineering Tianjin University

To achieve vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle(MJ-AUV) with the orthogonal joints structure was designed, and its virtual prototype was built using Automatic Dynamic Analysis of Mechanical systems(ADAMS). This paper focuses on the attitude control of the designed MJ-AUV which is a nonlinear system with strong coupling, big hysteresis, large inertia, and time-varying disturbance. According to the special structure of the MJ-AUV, an error based variable gain linear active disturbance rejection controller(EVG-LADRC) was proposed and compared with the traditional proportional-integral-derivative(PID) controller and LADRC. Simulation results demonstrated that EVG-LADRC outperformed the PID and LADRC in terms of rapidity, anti-interference, and accuracy.

关键词： MJ-AUV virtual prototype attitude control EVG-LADRC

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