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IEEE International Conference on robotics and Automation

作者： Erik Derner Jiri Kubalik Robert Babuska Department of Control Engineering Czech Technical University in Prague Prague Czech Republic Czech Institute of Informatics Czech Technical University in Prague Prague Czech Republic Cognitive Robotics Faculty of 3mE Czech Technical University in Prague Prague Czech Republic

Reinforcement learning (RL) is a suitable approach for controlling systems with unknown or time-varying dynamics. RL in principle does not require a model of the system, but before it learns an acceptable policy, it needs many unsuccessful trials, which real robots usually cannot withstand. It is well known that RL can be sped up and made safer by using models learned online. In this paper, we propose to use symbolic regression to construct compact, parsimonious models described by analytic equations, which are suitable for real-time robot control. Single node genetic programming (SNGP) is employed as a tool to automatically search for equations fitting the available data. We demonstrate the approach on two benchmark examples: a simulated mobile robot and the pendulum swing-up problem;the latter both in simulations and real-time experiments. The results show that through this approach we can find accurate models even for small batches of training data. Based on the symbolic model found, RL can control the system well.

关键词： Model learning for control AI-based methods Symbolic regression Reinforcement learning Optimal control

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Two Lyapunov methods in nonlinear state estimation via finite capacity communication channels

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IFAC-PapersOnLine 2017年第1期50卷 4132-4137页

作者： Matveev A. Pogromsky A. Department of Mathematics and Mechanics Saint Petersburg University St. Petersburg Russian Federation Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Control Systems and Industrial Robotics National Research University of Information Technologies Mechanics and Optics (ITMO) Saint-Petersburg Russian Federation

关键词： Entropy First Nonlinear systems second Lyapunov methods

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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

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. © 2018 IEEE.

关键词： Hamming distance

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Roster evaluation based on classifiers for the nurse rostering problem

arXiv

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arXiv 2018年

作者： Václavík, Roman Šůcha, Přemysl Hanzálek, Zdeněk Department of Control Engineering Faculty of Electrical Engineering Czech Technical University Karlovo náměstí 13 Prague 2121 35 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Zikova street 1903/4 Prague 6166 36 Czech Republic

The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the real-world instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100% correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Heuristic methods

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Energy optimization of robotic cells

arXiv

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arXiv 2018年

作者： Bukata, Libor Šůcha, Přemysl Hanzálek, Zdeněk Burget, Pavel Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Zikova 1903/4 Prague 6166 36 Czech Republic Faculty of Electrical Engineering Department of Control Engineering Czech Technical University in Prague Karlovo náměstí 13 Prague 212135 Czech Republic

This study focuses on the energy optimization of industrial robotic cells, which is essential for sustainable production in the long term. A holistic approach that considers a robotic cell as a whole toward minimizing energy consumption is proposed. The mathematical model, which takes into account various robot speeds, positions, power-saving modes, and alternative orders of operations, can be transformed into a mixed-integer linear programming formulation that is, however, suitable only for small instances. To optimize complex robotic cells, a hybrid heuristic accelerated by using multi-core processors and the Gurobi simplex method for piecewise linear convex functions is implemented. The experimental results showed that the heuristic solved 93 % of instances with a solution quality close to a proven lower bound. Moreover, compared with the existing works, which typically address problems with 3 to 4 robots, this study solved real-size problem instances with up to 12 robots and considered more optimization aspects. The proposed algorithms were also applied on an existing robotic cell in Škoda Auto. The outcomes, based on simulations and measurements, indicate that, compared with the previous state (at maximal robot speeds and without deeper power-saving modes), the energy consumption can be reduced by about 20 % merely by optimizing the robot speeds and applying power-saving modes. All the software and generated data sets used in this research are publicly available.90C10, 90C25 Copyright © 2018, The Authors. All rights reserved.

关键词： Industry 4.0

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Development and test verification of air temperature model for Chinese solar and Spainish Almeria-type greenhouses

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International Journal of Agricultural and Biological engineering 2017年第4期10卷 66-76页

作者： Jorge Antonio Sanchez-Molina Li Ming Francisco Rodriguez Jose Luis Guzman Wang Hui Yang Xinting The Automatic Control Robotics and Mechatronics Research GroupDepartment of InformaticsceiA3CIESOLUniversity of AlmeriaAlmeria 04120Spain Beijing Research Center for Information Technology in Agriculture/National Engineering Research Center for Information Technology in Agriculture/National Engineering Laboratory for Agri-product Quality Traceability/Key Laboratory for Information Technologies in Agriculture Ministry of AgricultureBeijing 100097China

Growth can be defined as an increment in biomass or an increment in weight or height of the organs of the plant influenced by physiological *** of these processes have their limits genetically determined,but climate and irrigation play an important *** of its importance,microclimate has been extensively studied in the modeling as a surrounding condition which is imposed by the exterior *** main objective of this work was to develop a temperature model based on the energy balance dynamics at two different greenhouse locations-South-eastern Spain and Northern China,and the traditional structures of Chinese solar greenhouse and Almería-type multi-span greenhouse were taken into *** final model was developed by combining the external conditions,the actuator influence and the crop growth,where the temperature is influenced by soil,crop,cover,actuators,back wall and greenhouse *** model took into account the energy lost by convective and conductive fluxes,as well as the energy supplied by solar radiation and heating *** soil and the back wall are the main media for energy *** temperature dynamic was determined by a physical model,which considered the energy balance from a holistic point of view-as a sub-model for a customizable interface among the external climate,the plant and the greenhouse *** influences of different subsystems included in the temperature model were analyzed and *** results showed a high R^(2)value of 0.94 for Beijing and 0.95 for Almeria,and the average error was low,of which the MAE and RMSE were 0.71 and 1.365 for Almeria and 0.62 and 1.102 for Beijing,***,the model can be considered as a powerful tool for control design purposes in microclimate systems.

关键词： air temperature model Chinese solar greenhouse Spanish Almería-type greenhouse energy balance dynamics microclimate

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Current trends in the development of intelligent unmanned autonomous systems

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Frontiers of Information Technology & Electronic engineering 2017年第1期18卷 68-85页

作者： Tao ZHANG Qing LI Chang-shui ZHANG Hua-wei LIANG Ping LI Tian-miao WANG Shuo LI Yun-long ZHU Cheng WU Department of Automation Tsinghua University Hefei Institute of Physical Science Chinese Academy of Sciences School of Control Science and Engineering Zhejiang University Robotics Institute Beihang University Shenyang Institute of Automation Chinese Academy of Sciences

Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence （AI）. The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and de- velopments in each area are introduced.

关键词： Intelligent unmanned autonomous system Autonomous vehicle Artificial intelligence robotics Development trend

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On designing an automated tool for capacitors removal from waste printed circuits boards

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IOP Conference Series: Materials Science and engineering 2019年第1期591卷

作者： C Stamate I Doroftei D Chirita A Burlacu 'Gheorghe Asachi' Technical University of Iasi Department of Mechanical Engineering Mechatronics and Robotics Blvd. Mangeron No. 43 700050 Iasi Romania 'Gheorghe Asachi' Technical University of Iasi Department of Automatic Control and Applied Informatics Blvd. Mangeron No. 27 700050 Iasi Romania

The mass electronics sector is one of the most important sources of waste, both in terms of volume and materials content with dangerous effects on the environment. Printed Circuit Boards (PCBs) are the most valuable component embedded into Electrical and Electronic Equipment (EEEs). The current amount of electronic systems is impressive while manual dismantling is a very common and non-efficient solution. On average, waste PCB accounts almost from 3% to 5% of the overall weight of waste electrical and electronic equipment. At this time, in Romania, the implemented solutions for waste PCB reduction are in a research phase, with no existing technological procedures that can be replicated. In the context of the TRADE-IT project we aim to design replicable architectures for waste printed boards reduction. Starting from the largest base plate and continuing to the smallest size, Extended ATX, ATX, Micro ATX, Mini ITX are some of the most used boards in a computer. When choosing a base plate from the ATX and/or microATX range, the shape factor must fit on a universal support bracket. The shape factor identifies the size of the circuit board, the location of the slots, and the location of the display board that comes out on the back of the computer. The shape factor also identifies the location of the holes that are used to mount the base plate in the universal clamp holder. Taking into account that capacitors represent approximately 8.6% of the PCB mass, we aim at designing an automated driven tool for their removal. The design is based on a robotic arm for motion manoeuvring, a programmable screwdriver and a custom-made end tool. In the current phase the proposed design was evaluated on controlled individual tests. The initial results highlight the feasibility of the proposed design while uncovering potential challenges for real-time implementation.

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Adaptive segmentation of multimodal polysomnography data for sleep stages detection 22

Adaptive segmentation of multimodal polysomnography data for...

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2017 22nd International Conference on Digital Signal Processing, DSP 2017

作者： Procházka, Aleš Kuchyňka, Jiri Yadollahi, Mohammadreza Araujo, Carmen Paz Suarez Vyšata, Oldřich University of Chemistry and Technology Department of Computing and Control Engineering Czech Republic Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Republic Charles University Department of Neurology Czech Republic Universidad de Las Palmas de Gran Canaria Spain

ISBN: (纸本)9781538618950

The paper presents a new algorithm for adaptive classification of sleep stages using multimodal data recorded in the sleep laboratory during overnight polysomnography records. The proposed method includes the learning process applied for the set of individuals with their sleep stages classified by an experienced neurologist. Features evaluated for time windows 30 s long and selected multimodal signals are used for construction and optimization of the proposed two-layer neural network model. Resulting computational system based upon breathing EEG and EOG features is used for analysis of new individuals to detect their sleep stages. Results include classification accuracy higher than 80% and 90% for Wake and REM stages, respectively. The proposed method can adaptively modify model coefficients to detect sleep stages and sleeping disorders using man-machine interaction. © 2017 IEEE.

关键词： Network layers

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Macroscopic robots as microscopic liquid molecules - Brownian motion and quantum dynamics

Macroscopic robots as microscopic liquid molecules - Brownia...

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SICE Annual Conference

作者： Teturo Itami Nobuyuki Matsui Noriaki Kouda Robotics Industry Development Council Fukuoka Japan Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Department of Control Engineering National Institute of Technology Shimane Japan

A maneuvering system by few numbers of "macroscopic" robots with neither apparatuses for mutual communication nor external sensors is given. Boltzmann distribution over energy of the robots allows us to calculate feedforward control input. Under the proposed feedforward control with PD compensation, system of few such robots is shown to make an object track a required path in a closed plane region. We show simulation studies on a system of four(N_0 = 4) robots. We also study planned movements by few macroscopic robots under quantum fluctuation.

关键词： Feedforward control model Swarm robotics Robots with poor equipments Boltzmann distribution Few robots Quantum dynamics

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