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Kinematic Comparison of Forward and Backward Swimming and Maneuvering in a Self-Propelled Sub-Carangiform Robotic Fish

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Journal of Bionic Engineering 2014年第2期11卷 199-212页

作者： Zhengxing Wu Junzhi Yu Min Tan Jianwei Zhang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 P. R. China Department of Informatics University of Hamburg Hamburg Germany

We make a thorough kinematic comparison of forward and backward swimming and maneuvering on a self-propelled robot platform that uses sub-carangifbrm swimming as the primary propulsor. An improved Central Pattern Generator （CPG） model allowing free adjustment of phase relationship and directional bias is employed to achieve flexible swimming and smooth transition. Considering the characteristics of forward swimming in carangiform fish and backward swimming in anguilliform fish, various backward swimming patterns for the sub-carangiform robotic fish are suitably created by reversing the direction of propagating propulsive waves. Through a combined use of the CPG control and closed-loop swimming direction control strategy, flexible and precise turning maneuvers in both forward and backward swimming are implemented and compared. By contrast with forward swimming, backward swimming requires a higher frequency or an increased lateral displacement to reach the same relative swimming speed. Noticeably, the phase difference shows a greater impact on forward swimming than on backward swimming. Our observations also indicate that the robotic fish achieves a larger turning rate in forward maneuvering than in backward maneuvering, yet these two maneuvers display comparable turning precision.

关键词： robotic fish backward swimming Central Pattern Generator （CPG） swimming locomotion control

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Dynamic modeling and control simulation of a modified delta manipulator

Dynamic modeling and control simulation of a modified delta ...

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International Conference on Information and Automation (ICIA)

作者： Jianlong Hao Xiaoliang Xie Guibin Bian Zhenqiu Feng Zhanjie Gao Zengguang Hou Hongnian Yu Luige Vladareanu State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Faculty of Science and Technology Bournemouth University Poole Dorset UK Robotics and Mechatronics Department Romanian Academy Romian

ISBN: (纸本)9781467391054

The delta parallel mechanism has proven to be a very successful parallel robot design and a number of variants have been proposed in the literature. Taking advantage of its simple construction and the intrinsic features such as high stiffness and accuracy, delta robots play an important role both in the fields of industrial robots and academical fields. In this paper, dynamic modeling and control simulation of a cable-driven modified delta manipulator for haptic interface are discussed. For the parallel mechanism is highly coupled and nonlinear, the dynamic model is of great importance for model-based controller to achieve a satisfactory performance. Based on the principle of virtual work, the dynamic equations of this manipulator are established. The proportional-derivative (PD) computed torque control scheme is proposed for trajectory tracking. With the help of the virtual prototype builded in ADAMS software and used in MATLAB software, the co-simulation is accomplished and the results validate the performance of the controller proposed in this paper.

关键词： Manipulator dynamics Joints Mathematical model Computational modeling Prototypes Trajectory

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Clique-based Cooperative Multiagent Reinforcement Learning Using Factor Graphs

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 248-256页

作者： Zhen Zhang Dongbin Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Department of Electric Engineering College of Automation EngineeringQingdao University

In this paper,we propose a clique-based sparse reinforcement learning(RL) algorithm for solving cooperative *** aim is to accelerate the learning speed of the original sparse RL algorithm and to make it applicable for tasks decomposed in a more general ***,a transition function is estimated and used to update the Q-value function,which greatly reduces the learning ***,it is more reasonable to divide agents into cliques,each of which is only responsible for a specific *** this way,the global Q-value function is decomposed into the sum of several simpler local Q-value *** decomposition is expressed by a factor graph and exploited by the general maxplus algorithm to obtain the greedy joint *** results show that the proposed approach outperforms others with better performance.

关键词： Sensors Games Learning (artificial intelligence) Approximation algorithms Algorithm design and analysis Heuristic algorithms Sparse matrices

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Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network

Continuous Travel Time Prediction for Transit Signal Priorit...

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International Conference on Intelligent Transportation

作者： Xiong Gang Wenwen Kang Feiyue Wang Fenghua Zhu Yisheng Lv Xisong Dong Jukka Riekki Susanna Pirttikangas State Key Lab. of Manage. & Control for Complex Syst. Institute of Automation Chinese Academy of Sciences Beijing Beijing CN The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Faculty of Information Technology and Electrical Engineering University of Oulu Finland

It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.

关键词： Vehicles Predictive models Object oriented modeling Training Artificial neural networks Data models

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Linguistic dynamic analysis for single lane's traffic status based on FCM

Linguistic dynamic analysis for single lane's traffic status...

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2014 International Conference on Informative and Cybernetics for Computational Social systems, ICCSS 2014

作者： Jie, Wang Hong, Mo Fenghua, Zhu College of Electrical AndInformation Engineering Changsha University of Science and Technology Changsha China State Key Laboratory of Management and Control for Complex System Beijing China

ISBN: (纸本)9781479947522

The results of traditional traffic status analysis are mostly single values, whose accuracy can't be determined;fuzzy c-means clustering (FCM) algorithm based on fuzzy theory can calculate the clustering center of plenty data quickly and easily;linguistic dynamic systems could describe the dynamic rules of complex systems in the language level. In this paper, membership functions are decided by FCM;result of a specific time period taken as one example is obtained;it's discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method. © 2014 IEEE.

关键词： Linguistics

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Differential Spatial Modulation for Dual-hop Amplify-and-Forward Relaying

Differential Spatial Modulation for Dual-hop Amplify-and-For...

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IEEE International Conference on Communications

作者： Meng Zhang Miaowen Wen Xiang Cheng Liu-Qing Yang School of Electronics Engineering and Computer Science Peking University School of Electronic and Information Engineering South China University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467364300

Differential spatial modulation (DSM) is a newly proposed differential modulation technique tailored to spatial modulation (SM), which requires no channel state information (CSI) at the receiver. DSM can offer flexible tradeoff between the reception reliability and the system complexity. In this paper, we are the first to study the adoption of DSM in a dual-hop amplify-and-forward (AF) relaying system, which consists of a two-antenna source, a single-antenna relay, and a single-antenna destination, so as to reduce the burden of channel tracking on both the relay and the destination. We derive a general upper bound on the average bit error probability (ABEP) achieved by the system. Moreover, an exact closed-form ABEP expression and the asymptotic result are provided for BPSK signaling in Rayleigh fading environment. The same system setup with the adoption of SM at the source is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a 3dB signal-to-noise power ratio (SNR) penalty of the considered system compared with the benchmark.

关键词： Relays Upper bound Binary phase shift keying Signal to noise ratio Transmitting antennas Wireless communication

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Distributed Estimation for Radio Environment Map in Cognitive Radio Networks 33

Distributed Estimation for Radio Environment Map in Cognitiv...

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第三十三届中国控制会议

作者： LIU Jianzhi HAN Hui CHEN Cailian LI Li'an GUAN Xinping The State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

ISBN: (纸本)9789881563842

Radio Environment Map(REM)is a map that indicates the radio signal strength(RSS)over a finite geographical *** enables cognitive radio(CR)users to opportunistically access to the spectrum unoccupied by the primary users(PUs)who are licensed to use specific *** this paper,a basis expansion based scheme is presented to estimate *** take the sparsity of preselected basis functions and the inherent time-varying spectrum usage of primary users into account such that the RSS estimation is formulated as a recursive least squares(RLS)problem with an l1norm constraint.A distributed algorithm is presented to solve the RLS *** SU only need to exchange information with its one-hop neighbours to sketch the REM with radio signal ***,simulation studies are presented to validate the performance of the proposed method.

关键词： Cognitive radio,Basis expansion,Cooperative spectrum sensing,Distributed estimation

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Learning associate appearance manifolds for cross-pose face recognition

Learning associate appearance manifolds for cross-pose face ...

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作者： Chen, Xue Wang, Chunheng Xiao, Baihua Cai, Xinyuan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781479957514

Pose variation is a major challenge in face recognition. In this paper, we propose a novel cross-pose face recognition method by learning associate appearance manifolds to model the connection of faces under different poses. The associate manifolds are built on an auxiliary set, in which each identity contains cross-pose face images. The basic assumption is that cross-pose face images from two similar identities can be projected onto similar appearance manifolds by pose-specific transforms. We first associate the input faces with alike identities from the auxiliary set. Then the manifolds of cross-pose faces in the training set are confined close to that of the associate identities in the auxiliary set. Thus, the connection of cross-pose faces is well modeled by the associate appearance manifolds on the auxiliary set. Formally, we formulate the assumption as a manifold-based distance minimization problem, so as to learn the optimal transforms. Experiments on the Multi-PIE dataset demonstrate the effectiveness of the proposed method. © 2014 IEEE.

关键词： Face recognition

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Scenario oriented discriminant analysis for still-to-video face recognition

Scenario oriented discriminant analysis for still-to-video f...

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作者： Chen, Xue Wang, Chunheng Xiao, Baihua Cai, Xinyuan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781479957514

In the Still-to-Video (S2V) face recognition, each subject is enrolled with only few high resolution images, while the probe is video clips of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. Therefore, in this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenario patterns (still, video), and further pursue a common discriminant space for the cross-scenario samples. To maximize the intra-individual correlation of samples in the mapping space, we formulate the learning objective by incorporating the intra-class compactness and the inter-class dispersion. The gradient descend algorithm is used to get the optimal solution. Experimental results on the COX-S2V dataset demonstrate the effectiveness of the proposed method and remarkable superiority over state-of-art methods. © 2014 IEEE.

关键词： Discriminant analysis

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Learning co-occurrence strokes for scene character recognition based on spatiality embedded dictionary

Learning co-occurrence strokes for scene character recogniti...

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作者： Gao, Song Wang, Chunheng Xiao, Baihua Shi, Cunzhao Zhou, Wen Zhang, Zhong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781479957514

Robust scene-text-extraction system can be used in lots of areas. In this work, we propose to learn co-occurrence of local strokes for robust character recognition by using a spatiality embedded dictionary (SED). Different from spatial pyramid partitioning images into grids to incorporate spatial information, our SED associates every codeword with a particular response region and introduces more precise spatial information for character recognition. After localized soft coding and max pooling of the first layer, a sparse dictionary is learned to model co-occurrence of several local strokes, which further improves classification performance. Experiment on benchmark datasets demonstrates the effectiveness of our method and the results outperform state-of-the-art algorithms. © 2014 IEEE.

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