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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Hao, Rong Zhang, Jia Xin, Bin Chen, Chen Dou, Lihua School of Automation Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781450357647

The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively. © 2018 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms

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Preface——Special Issue to Celebrate the 30th Anniversary of Journal of systems Science and complexity

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Journal of systems Science & complexity 2017年第1期30卷 1-3页

作者： GAO Xiao-Shan CHEN Jie SHAO Jun WANG Shouyang Academy of Mathematics and Systems Science Chinese Academy of Sciences Key Laboratory of Intelligent Control and Design of Complex Systems Beijing Institute of Technology Department of Statistics University of Wisconsin-Madison

This special issue is dedicated to the 30th anniversary of Journal of systems Science and complexity(JSSC)which is under the auspices of the Institute of systems Science,Chinese Academy of *** of systems Science,Chinese Academy of Sciences was established in 1979 by scientists Chao-Chih Kwan,Wen-Tsun Wu,Guo-Zhi *** 1981,the Journal of systems

关键词：系统科学复杂性杂志中国科学院序言科学研究所

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Artificial intelligence enhances the performance of chaos-based wireless communication

arXiv

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arXiv 2019年

作者： Ren, Hai-Peng Zhao, Hong-Er Bai, Chao Yin, Hui-Ping Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

Some new findings for chaos-based wireless communication systems have been identified recently. First, chaos has proven to be the optimal communication waveform because chaotic signals can achieve the maximum signal to noise ratio at receiver with the simplest matched filter. Second, the information transmitted in chaotic signals is not modified by the multipath wireless channel. Third, chaos properties can be used to relief inter-symbol interference (ISI) caused by multipath propagation. Although recent work has reported the method of obtaining the optimal threshold to eliminate the ISI in chaos-based wireless communication, its practical implementation is still a challenge. By knowing the channel parameters and all symbols, especially the future symbol to be transmitted in advance, it is almost an impossible task in the practical communication systems. Owning to Artificial intelligence (AI) recent developments, Convolutional Neural Network (CNN) with deep learning structure is being proposed to predict future symbols based on the received signal, so as to further reduce ISI and obtain better bit error rate (BER) performance as compared to that used the existing sub-optimal threshold. The feature of the method involves predicting the future symbol and obtaining a better threshold suitable for time variant channel. Numerical simulation and experimental results validate our theory and the superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Parallel Ant Colony Optimization for the Electric Vehicle Routing Problem

Parallel Ant Colony Optimization for the Electric Vehicle Ro...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Changhe Li Georgios Ellinas Marios Polycarpou KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation and Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Parallelizing metaheuristics has become a common practice considering the computation power and resources available nowadays. The aim of parallelizing a metaheuristic is either to increase the quality of the generated output, given a fixed computation time, or to reduce the required time in generating an output. In this work, we parallelize one of the best-performing ant colony optimization (ACO) algorithms and apply it to the electric vehicle routing problem (EVRP). EVRP is more challenging than the conventional vehicle routing problem, as with the consideration of electric vehicles additional hard constraints arise within the EVRP due to their limited driving range (e.g., the consideration whether electric vehicles need to visit a charging station during their daily operation). The proposed parallel ACO algorithm with several colonies also uses a migration policy to allow communication between the different colonies. From the simulation studies it is shown that parallelizing ACO algorithms, both with and without a migration policy, is highly effective.

关键词： Electric vehicles Routing Batteries Technological innovation Vehicle routing Charging stations Optimization

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Motion Planning for Heterogeneous Unmanned systems under Partial Observation from UAV

arXiv

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arXiv 2020年

作者： Chen, Ci Wan, Yuanfang Li, Baowei Wang, Chen Xie, Guangming Jiang, Huanyu College of Biosystems Engineering and Food Science Zhejiang University Hangzhou310058 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China Department of Ocean Science and Engineering Southern University of Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs’ vast view scope. However, due to UAVs flight altitude limitations, it may be impossible to observe the global map, and motion planning in the local map is a POMDP (Partially Observable Markov Decision Process) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned system under partial observation from UAV without reconstruction of global maps, which consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV’s perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Facial Expression Emotion Recognition Based Human-robot Interaction System

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 668-676页

作者： Zhentao Liu Min Wu Weihua Cao Luefeng Chen Jianping Xu Ri Zhang Mengtian Zhou Junwei Mao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems IEEE

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

关键词： Emotion generation facial expression emotion recognition(FEER) human-robot interaction(HRI) system design

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A thermoplastic elastomer belt based robotic gripper

arXiv

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arXiv 2020年

作者： Zheng, Xingwen Hou, Ningzhe Dinjens, Pascal Johannes Daniël Wang, Ruifeng Dong, Chengyang Xie, Guangming The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China The Department of Electrical and Electronic Engineering The University of Manchester ManchesterM1 3BB United Kingdom Fontys University of Applied Sciences Eindhoven5612 AP Netherlands The Institute of Ocean Research Peking University Beijing100871 China The Peng Cheng Laboratory Shenzhen518055 China

Novel robotic grippers have captured increasing interests recently because of their abilities to adapt to varieties of circumstances and their powerful functionalities. Differing from traditional gripper with mechanical components-made fingers, novel robotic grippers are typically made of novel structures and materials, using a novel manufacturing process. In this paper, a novel robotic gripper with external frame and internal thermoplastic elastomer belt-made net is proposed. The gripper grasps objects using the friction between the net and objects. It has the ability of adaptive gripping through flexible contact surface. Stress simulation has been used to explore the regularity between the normal stress on the net and the deformation of the net. Experiments are conducted on a variety of objects to measure the force needed to reliably grip and hold the object. Test results show that the gripper can successfully grip objects with varying shape, dimensions, and textures. It is promising that the gripper can be used for grasping fragile objects in the industry or out in the field, and also grasping the marine organisms without hurting them. Copyright © 2020, The Authors. All rights reserved.

关键词： Grippers

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Multi-robot Path Planning and Trajectory Smoothing

Multi-robot Path Planning and Trajectory Smoothing

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Hub Ali Gang Xiong Huaiyu Wu Bin Hu Zhen Shen Hongxing Bai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China Hubei Jointown Technology Co. Ltd Artificial Intelligence Research Institute Wuhan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.

关键词： Robot kinematics Trajectory Collision avoidance Task analysis Resource management

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Short-term load forecasting based on GA-BPNN-SVR optimization algorithm 8

Short-term load forecasting based on GA-BPNN-SVR optimizatio...

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： He, Lianghua Ge, Lai Zhang, Hao Wang, Guowei School of automation China University of Geosciences Wuhan Hubei430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei430074 China

In order to improve the accuracy of short-term power load forecasting, an algorithm that simultaneously optimizes SVR parameters and optimizes the selection of input features is proposed. Firstly, the input features are selected by BP neural network. In order to avoid the influence of the randomness of the initial weights and thresholds on the optimization, the GA algorithm is used to optimize the weights and thresholds. Secondly, using GA algorithm to optimize the parameters of support vector regression, a short-term load forecasting model based on GA-BPNN-SVR optimization algorithm is obtained. Finally, the GA-BPNN-SVR model is applied to practical short-term power load forecasting, and the forecasting effect is compared with that of GA-SVM and BPNN-SVR models. The results show that the average absolute percentage error obtained by this method is only 1.74%, which is 1.64% and 3.11% lower than the average absolute percentage error obtained with GA-SVM model and BPNN-SVR. This algorithm can be effectively used for short-term load forecasting of power systems. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Genetic algorithms

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3D Deep Learning for 3D Printing of Tooth Model

3D Deep Learning for 3D Printing of Tooth Model

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yiwen Liu Xiuqin Shang Zhen Shen Bin Hu Zhihai Wang Gang Xiong Faculty of Information The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Faculty of Information Beijing University of Technology Beijing China

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives.

关键词： Three-dimensional displays Deep learning Transforms Cameras Object segmentation Image segmentation Sensors

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