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ieee Latin American Robotics symposium, LARS

作者： Wilson Eduardo Barioni Igor Pardal Latini André Lazzaretti Marco Teixeira Flavio Neves Lucia Valeria Ramos De Arruda Automation and Advanced Control System Laboratory (LASCA) Graduate Program in Electrical and Computer Engineering (CPGEI) Universidade Tecnologica Federal do Parana (UTFPR) Curitiba Parana Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Automated Guided Vehicles (AGVs) are usual in industrial settings, with an increasing economic impact on processes. They move through numerous environments inside factories, commonly navigating long distances, while performing several activities. The constant detection of such vehicles, especially in cases of maintenance and safety, is a main issue in an industry setting. Usually, this information could be provided by a supervisory system, but many applications are not so large as to make such a system viable. Thus, a solution via machine learning and computer vision is developed in this work, by using simple cameras, such as the factories' security cameras. As the industrial environment is a scenario with a lot of variation and noise, the Transfer Learning technique is used to improve the training step of the developed AGV detection system. Finally, a database with 1067 images is used to build and validate the model, achieving a result greater than 80% of F1-score for various confidence values.

关键词： Training Computer vision Remotely guided vehicles computational modeling Transfer learning Cameras Production facilities

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Adversarial Feature Sampling Learning for Efficient Visual Tracking

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2020年第2期17卷 847-857页

作者： Yin, Yingjie Xu, De Wang, Xingang Zhang, Lei Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing 100190 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The tracking-by-detection tracking framework usually consists of two stages: drawing samples around the target object and classifying each sample as either the target object or background. Current popular trackers under this framework typically draw many samples from the raw image and feed them into the deep neural networks, resulting in high computational burden and low tracking speed. In this article, we propose an adversarial feature sampling learning (AFSL) method to address this problem. A convolutional neural network is designed, which takes only one cropped image around the target object as input, and samples are collected from the feature maps with spatial bilinear resampling. To enrich the appearance variations of positive samples in the feature space, which has limited spatial resolution, we fuse the high-level features and low-level features to better describe the target by using a generative adversarial network. Extensive experiments on benchmark data sets demonstrate that the proposed ASFL achieves leading tracking accuracy while significantly accelerating the speed of tracking-by-detection trackers. Note to Practitioners-Visual tracking can be applied in many intelligent automation systems, such as robotic intelligent navigation system, intelligent human-computer interaction system, and so on. In a robotic intelligent navigation system, visual tracking can generate target's motion trajectory from image sequences. Visual tracking can also obtain body movement information automatically during the interactive process in the intelligent human-computer interaction system. Accuracy and speed are two key indicators for visual tracking, and intelligent automation systems usually need a tracker with more accuracy and faster speed. This article aims to develop a fast and accurate tracking method by adversarial feature sampling learning (AFSL). In the concrete implementation process, AFSL gets samples by sampling in the feature space rather than on raw images to

关键词： Adversarial learning deep convolution neural network feature sampling visual tracking

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control System based on Reinforcement Learning applied to a Klatt-Engell Reactor

Control System based on Reinforcement Learning applied to a ...

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ieee International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Estrada Rayme, Leighton Leandro Cardenas Lizana, Paul Antonio Univ Nacl Ingn Fac Ingn Elect & Elect Lima Peru Univ Ingn & Tecnol UTEC Dept Bioengn Lima Peru

ISBN: (纸本)9781728199047

A control system based in Reinforcement Learning (RL) is developed to control a two inputs-two outputs (TITO) system, which is a Klatt-Engell reactor. In the design of the control system, it will be based on a learning method called Model Free Reinforcement Learning (MLFC). Firstly, it will show the model math of the plant to build the nonlinear state equation. Then, it will design the controller based on two sub-controllers MLFC, selecting the characteristics of the states, actions, rewards and other parameters of RL. Finally, it will perform the simulations of the control system for references functions, stepped variables and sine.

关键词： Reinforcement learning controller system control states action rewards q-learning reactor klatt engell nonlinear temperature component

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计及P2P市场产消者灵活性的配电网阻塞管理

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电力系统保护与控制 2024年第15期52卷 91-104页

作者：周玮王誉颖芝昕雨彭飞翔许连春高纪阳大连理工大学电气工程学院辽宁大连116024 国网重庆市电力公司市南供电分公司重庆401336 安徽大学电气工程与自动化学院安徽合肥230601 国网辽宁省电力有限公司大连供电公司辽宁大连116001

多点接入的柔性负荷和可再生分布式电源会使配网潮流大小和方向频繁变动,而配电侧产消者通常仅考虑自身效益最大为目标进行端对端(peer-to-peer,P2P)电能交易,忽略了不同设备能量流动对系统安全运行的影响,使得交易结果极易导致网络阻... 详细信息

多点接入的柔性负荷和可再生分布式电源会使配网潮流大小和方向频繁变动,而配电侧产消者通常仅考虑自身效益最大为目标进行端对端(peer-to-peer,P2P)电能交易,忽略了不同设备能量流动对系统安全运行的影响,使得交易结果极易导致网络阻塞。针对采用连续双向拍卖机制和零智商增强型投标策略的分散式P2P交易模式所引起的配网阻塞,兼顾产消者数据隐私管理和消费心理学影响,提出一种两级市场动态协调阻塞管理方法。该方法以产消者综合效益最大和解决网络阻塞调节量最小为目标,利用Distflow潮流模型,构建基于节点边际电价的节点灵活性辅助服务价格计算模型。进而通过分布式优化算法求解得到阻塞成本,将该成本按照P2P交易时序和线路阻塞贡献度分摊至有责任的产消者,实现阻塞消除。算例采用ieee 33节点改进的11节点配网系统,结果表明该方法可在设定的时间尺度和计算精度条件下有效解决网络阻塞,降低阻塞成本和分散式P2P电能交易的效益损失。

关键词： P2P能源交易配电网阻塞管理分布式能源灵活性辅助服务

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Parameterizable and Jerk-Limited Trajectories with Blending for Robot Motion Planning and Spherical Cartesian Waypoints

Parameterizable and Jerk-Limited Trajectories with Blending ...

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ieee International Conference on Robotics and automation (ICRA)

作者： Jianjie Lin Markus Rickert Alois Knoll Robotics Artificial Intelligence and Real-Time Systems Technische Universität München Munich Germany

This paper presents two different approaches to generate a time local-optimal and jerk-limited trajectory with blends for a robot manipulator under consideration of kinematic constraints. The first approach generates a trajectory with blends based on the trapezoidal acceleration model by formulating the problem as a nonlinear constraint and a non-convex optimization problem. The resultant trajectory is locally optimal and approximates straight-line movement while satisfying the robot manipulator’s constraints. We apply the bridged optimization strategy to reduce the computational complexity, which borrows an idea from model predictive control by dividing all waypoints into consecutive batches with an overlap of multiple waypoints. We successively optimize each batch. The second approach is a combination of a trapezoidal acceleration model with a 7-degree polynomial to form a path with blends. It can be efficiently computed given the specified blending parameters. The same approach is extended to Cartesian space. Furthermore, a quaternion interpolation with a high degree polynomial under consideration of angular kinematics is introduced. Multiple practical scenarios and trajectories are tested and evaluated against other state-of-the-art approaches.

关键词： Interpolation computational modeling Quaternions Kinematics Trajectory Time factors computational complexity

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Multi-objective vehicle path planning method for recycling process of used electrical and electronic products

Multi-objective vehicle path planning method for recycling p...

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2020 ieee International symposium on Product Compliance Engineering-Asia, ISPCE-CN 2020

作者： Yang, Cuili Wu, Zhanhong Han, Honggui Bi, Xiaowen Beijing University of Technology Faculty of Information Technology Beijing China City University of Hong Kong Department of Electrical Engineering Hong Kong Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligence System Eng. Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Department of Electrical Engineering City University of Hong Kong. Hong Kong

ISBN: (纸本)9781665414616

During the recycling process of used electrical and electronic products from recycling places to sorting center, the vehicle path planning is an urgent problem to be solved which needs to minimize the number of vehicles and vehicle transportation distance simultaneously. To solve this problem, a multi-objective optimization algorithm is proposed which combines the genetic algorithm (GA) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Firstly, the vehicle path planning model of the sorting center is established according to characteristics of product transportation process. Secondly, the GA is used to produce some vehicle path planning schemes. Then, the MOEA/D is introduced to deal with the trade-off relationship between two objectives. Simulation results demonstrate that the proposed algorithm is able to generate a set of non-dominated solutions. © 2020 ieee.

关键词： Genetic algorithms

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Application of Virtualization Technologies in Novel Industrial automation: Catalyst or Show-Stopper? 18

Application of Virtualization Technologies in Novel Industri...

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18th ieee International Conference on Industrial Informatics (INDIN)

作者： Gundall, Michael Reti, Daniel Schotten, Hans D. German Res Ctr Artificial Intelligence GmbH DFKI Kaiserslautern Germany Tech Univ Kaiserslautern Dept Elect & Comp Engn Kaiserslautern Germany

ISBN: (纸本)9781728149646

Industry 4.0 describes an adaptive and changeable production, where its factory cells have to be reconfigured at very short intervals, e.g. after each workpiece. Furthermore, this scenario cannot be realized with traditional devices, such as programmable logic controllers. Here the use of well-proven technologies of the information technology are conquering the production hall (IT-OT convergence). Therefore, both virtualization and novel communication technologies are being introduced in the field of industrial automation. In addition, these technologies are seen as the key for facilitating various emerging use cases. However, it is not yet clear whether each of the dedicated hardware and software components, which have been developed for specific control tasks and have performed well over decades, can be upgraded without major adjustments. In this paper, we examine the opportunities and challenges of hardware and operating system-level virtualization based on the stringent requirements imposed by industrial applications. For that purpose, benchmarks for different virtualization technologies are set by determining their computational and networking overhead, configuration effort, accessibility, scalability, and security.

关键词： Smart Manufacturing Virtualization Technologies Container Virtual Machines automation Systems Networking Performance Benchmarking Industrial Security

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Investigating Neural Network Architectures, Techniques, and Datasets for Autonomous Navigation in Simulation

Investigating Neural Network Architectures, Techniques, and ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Oliver Chang Christiana Marchese Jared Mejia Anthony J. Clark Pomona College Claremont California USA

ISBN: (纸本)9781728190495

Neural networks (NNs) are becoming an increasingly important part of mobile robot control systems. Compared with traditional methods, NNs (and other data-driven techniques) produce comparable-if not better-results while requiring less engineering knowhow. Training NNs, however, still requires exploration of a significant number of architectural, optimization, and evaluation options. In this study, we build a simulation environment, generate three image datasets using distinct techniques, train 652 models (including replicates) using a variety of architectures and paradigms (e.g., classification, regression, etc.), and evaluate the navigation ability of the model in simulation. Our goal is to explore a large number of model possibilities so that we can select the most promising for future study with a physical device. Training datasets leading to the best performing models were those that included a significant amount of noise from seemingly inefficient actions. The most promising models explicitly incorporated “memory” wherein previous actions were included as an input in the next step. Such models performed as good or better than conventional convolutional NNs, recurrent NNs, and custom architectures including two camera frames. Although trained models perform well in an environment matching the distribution of the training dataset, they fail when the simulation environment is altered in a seemingly insignificant manner. In robotics research it is often taken for granted that a model with good validation characteristics will perform well on the underlying task, but the results presented here show that there can often be a loose relationship between validation metrics and performance.

关键词： Training Performance evaluation Navigation computational modeling Robot vision systems Computer architecture Mobile robots

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Cooperative Routing for an Air-Ground Vehicle Team-Exact Algorithm, Transformation Method, and Heuristics

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2020年第1期17卷 537-547页

作者： Manyam, Satyanarayana G. Sundar, Kaarthik Casbeer, David W. Infoscitex Corp Dayton OH 45431 USA Ctr Nonlinear Studies Los Alamos NM 87544 USA US Air Force Res Lab Control Sci Ctr Excellence Wright Patterson AFB OH 45433 USA

This paper considers a cooperative vehicle routing problem for an intelligence, surveillance, and reconnaissance mission in the presence of communication constraints between the vehicles. The proposed framework uses a ground vehicle and an unmanned aerial vehicle (UAV) that travel cooperatively and visit a set of targets while satisfying the communication constraints. The problem is formulated as a mixed-integer linear program, and a branch-and-cut algorithm is developed to solve the problem to optimality. Furthermore, a transformation method and a heuristic are also developed for the problem. The effectiveness of all the algorithms is corroborated through extensive computational experiments on several randomly generated instances. Note to Practitioners-This paper is motivated by an intelligence, surveillance, and reconnaissance mission involving a single unmanned aerial vehicle (UAV) and a ground vehicle, where the vehicles must coordinate their activity in the presence of communication constraints. The combination of a small UAV and a ground vehicle is an ideal platform for such missions, since small UAVs can fly at low altitudes and can avoid obstacles or threats that would be problematic for the ground vehicle alone. This paper addresses the coordinated routing problem involving these two vehicles and presents an algorithm to obtain an optimal solution for this problem, fast heuristics to obtain good feasible solutions, and also a transformation method to transform any instance of this cooperative vehicle routing problem to an instance of the one-in-a-set traveling salesman problem.

关键词： Branch-and-cut communication constraints cooperative vehicle routing heuristics mixed-integer linear program path planning unmanned aerial vehicles (UAVs)

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Lithology Identification using Well Logging Images Based on Improved Inception Network

Lithology Identification using Well Logging Images Based on ...

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ieee symposium Series on computational intelligence (SSCI)

作者： Rui Shan Qiang Xing Jinyan Zhang Jun Wang Yaniiana Wang Baodi Liu College of Oceanography and Space Informatics China University of Petroleum(East China) Qingdao China Well Logging Company of Sinopec Shengli Petroleum Engineering Co. LTD Dongying China College of Control Science and Engineering China University of Petroleum(East China)) Qingdao China

ISBN: (纸本)9781728190495

The lithology identification plays a fundamental role for the fine characterization and comprehensive evaluation of complex reservoirs. Several methods so far have been proposed for lithology identification such as the analysis of sensitive conventional logging curves, intersection diagrams as well as geostatistical analysis. Though these methods can lead to improving accuracy of lithology recognition, they are either indirectly reflect lithology information or affected by handwork factors, thus the efficiency and accuracy are limited. Recently, in the image processing area, Inception convolutional neural network has become a powerful tool for expressing complex structures and extracting multi-scale feature information due to its parallel structure of convolutional kernel. To this end, in this paper, we propose to apply the Inception network for lithology identification based on high-resolution well-logging imaging. Different from the conventional Inception network, the proposed model is able to automatically extract the typical feature regions of different lithology categories from the raw Formation MicroScanner Image (FMI) data by parameter transfer strategy, which could improve the generalization performance and enhance the accuracy of lithology identification. To alleviate the problem of differences in the distribution of features, the improved Inception model adopts regularized loss function constrained full connection layer to extract high-level features and obtain more available and valuable information. Experimental results show that the network model has the advantages of fewer parameters and less computational load, and the accuracy of lithology identification can reach 97.32%.

关键词： Training computational modeling Imaging Feature extraction Transformers Reservoirs Data mining

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