2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)最新文献

{"title":"Research on Command Confirmation Unit Based on Motor Imagery EEG Signal Decoding Feedback in Brain-Computer Interface","authors":"Yue Zhang, Weihai Chen, Chun-Liang Lin, Jun-Uk Chu, F. Meng","doi":"10.1109/ICARCV.2018.8581088","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581088","url":null,"abstract":"The brain-computer interface (BCI) technology is a new human-machine interaction technology that realizes people to control external devices directly by thinking (i.e. electroencephalogram, EEG). However, because of the weakness and randomness of EEG signal, it is very complicated and difficult to process and identify the EEG signal recorded by the non-invasive BCI, and the decoding error often occurs. In view of the brain electrical signal decoding error, an experimental paradigm for simultaneous acquisition of spontaneous EEG and evoked EEG was designed, where the subjects generated the error-related potentials (ErrP) based on the decoded feedback of motor imagery EEG. We analyzed the EEG signal two times. The motor imagery EEG, which was the component of the EEG signal, was analyzed at the first time analysis. We classified the motor imagery EEG signal of left and right hand, then analyzed the classification method quantitatively using the Receiver Operating Characteristic (ROC) curves and the area under the curve (AVC). Although the EEG signal were influenced greatly by the individual difference, the AVC values can still reach more than 0.7. Meanwhile, the frequency domain characteristics were analyzed. The activation brain regions of the left-right hand motor imagery are mainly concentrated in the area of the perceptual motor cortex where is responsible for hand motion, but they will also be influenced by the artifacts of the surrounding channels. In the second time analysis, the ErrP was extracted and discussed. Its latency, waveform and amplitude characteristics were studied in the time domain and then a suitable classifier is selected by comparing a variety of classifiers, which classification accuracy is up to 90%. Therefore, the research based on the ErrP signal played a theoretical foundation for applying to the lower limb exoskeleton rehabilitation robot in the future, and ensured the feasibility of applying the command confirmation unit based on ErrP signal to the exoskeleton rehabilitation robot.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133087027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constrained Model Predictive Control using Kinematic Model of Vehicle Platooning in VISSIM Simulator","authors":"Rongkai Zhang, Anuj Abraham, Soumya Dasgupta, J. Dauwels","doi":"10.1109/ICARCV.2018.8581314","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581314","url":null,"abstract":"Driving Heavy Duty Vehicles (HDVs) as a platoon has potential to significantly reduce the fuel consumption, human labor and increase the safety. A suitable controller which can maintain the vehicle movement in a defined topology is essential for HDV platooning. This paper proposes a controller based on the combination of Constant Distance (CD) and Headway Time (HT) topologies using Model Predictive Control (MPC) for a longitudinal HDVs platoon. In addition to this, a MPC controller is compared with conventional PID (Proportional-Integral-Derivative) controller. The controller aims to maintain intervehicular distance and headway time between the vehicles for two cases, namely unconstrained and constrained optimization problem. The predictive control algorithm uses a kinematic model of vehicle platooning. A systematic handling of constraints yields significant improvements in the performance of the proposed MPC strategy over conventional PID controller. A road network of a U.S. freeway I5 has been built in VISSIM for the simulations. The results and discussions are at the end of the paper.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123907074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Autonomous Forklift with 3D Time-of-Flight Camera-Based Localization and Navigation","authors":"Ulrich Behrje, Marian Himstedt, E. Maehle","doi":"10.1109/ICARCV.2018.8581085","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581085","url":null,"abstract":"In this paper we present an autonomous guided vehicle (AGV) based on a forklift. While centralized transport systems are widely used in the industry, these systems are expensive to set up and inflexible with regard to changes in the schedule. We in contrast investigate an approach that uses a decentralized autonomous forklift that uses a 3D Time-of-Flight (ToF) camera as its navigation sensor and is not dependent on artificial visual landmarks. The capability to process transport orders and maneuver in confined space with the required accuracy has been successfully tested in two warehouse environments.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127671292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Accuracy Numerical System for Fourth-order Fractional Diffusion-wave Model","authors":"Xuhao Li, Patricia J. Y. Wong","doi":"10.1109/ICARCV.2018.8581358","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581358","url":null,"abstract":"In this paper, we tackle the numerical treatment of a fourth-order fractional diffusion-wave problem using parametric quintic spline. It is shown that the numerical scheme is stable and convergent and the theoretical convergence order improves those of earlier work. To confirm, simulation is carried out to demonstrate its efficiency.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121266150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRAC-MU Online Learning","authors":"Siyun Zhang, Jian-wei Liu, Xin Zuo, X. Wan, M. Kamel","doi":"10.1109/ICARCV.2018.8581389","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581389","url":null,"abstract":"In this paper, we apply the method of control theory to machine learning, proposing a new multiplication update algorithm combined with adaptive control theory, we name it MRAC-MU algorithm. A new parameter updating law is obtained according to Lyapunov stability theorem. Using the same object function as the exponential gradient (EG) algorithm, which is the key online learning method to multiplicative updates algorithm, Experiments are used to validate the proposed algorithm has a better result than EG algorithm in prediction accuracy.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Vehicle Localization Based on Surveyed-Maps in Urban Environment","authors":"Syed Zeeshan Ahmed, Kun Zhang, V. B. Saputra, C. H. Pang, Y. Chen","doi":"10.1109/ICARCV.2018.8581255","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581255","url":null,"abstract":"With the development of high reliability sensors, autonomous vehicles (AV) operating in real urban environments have been made possible. However, there still remain many challenges in improving the robustness and accuracy of AV localization in urban environments. In this paper, we propose a group of optimization techniques to make reliable map-based localization possible, using sparse point clouds from low cost light detection and ranging (LIDAR) sensors and odometry sensors. The map based Monte Carlo Localization (MCL) makes use of vertical and intensity features in it's observation model along with Heuristic Resampling to achieve robust localization. The proposed Heuristic Resampling picks the best candidate particle for localization output and resamples the remaining particles based on a defined heuristic. In addition, the LIDAR Calibration and Motion Compensation methods further improve localization accuracy. Experiments have been carried out to validate the effectiveness of the proposed techniques.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115838526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An EEG-Based BCI System for Controlling Lower Exoskeleton to Step Over Obstacles in Realistic Walking Situation","authors":"Xingguo Long, Du-Xin Liu, Shuang Liang, Zefeng Yan, Xinyu Wu","doi":"10.1109/ICARCV.2018.8581094","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581094","url":null,"abstract":"The strategies to adopt brain-computer interfaces (BCIs) to drive assisted devices are proved to be feasible in many studies. Although several studies focus on detecting the initiation of normal walking by BCIs, few consider how to distinguish the change of gait pattern for different terrains in a realistic walking situation. Therefore, this paper proposes an innovative experimental paradigm for robust control of exoskeleton based on a BCI system. Several pseudo online trials are conducted to prove the feasibility of the proposed paradigm. Firstly, a labeled windows generator (LWG) is built to produce electroencephalogram (EEG) windows based on acquired gait data and EEG data. Then the common spatial pattern (CSP) is used to extract features from the labeled EEG windows. Finally, a support vector machine (SVM) classifier is trained to predict the intention of the subject. The experimental results corroborate the feasibility of obtaining the intention of stepping over obstacles from normal walking through the proposed BCI-controlled exoskeleton system.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"403 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117096697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"$boldsymbol{H}_{infty}$ Filtering for Delta Operator Networked Systems with Random Delays and Limited Communication","authors":"Duanjin Zhang, Yinshuang Zhang, Xiaobei Gao","doi":"10.1109/ICARCV.2018.8581324","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581324","url":null,"abstract":"This paper is concerned with the problem of $boldsymbol{H}_{infty}$ filtering for networked control systems. A Bernoulli distributed is used to represent random time-delays and a switched sequence to illustrate limited communication in the considered system. Sufficient conditions that make the filtering error system to be exponentially stable with $boldsymbol{H}_{infty}$ performance, are proposed in terms of linear matrix inequalities (LMI) and Lya-punov-Krasovskii functional in delta domain. The parameters of the designed $boldsymbol{H}_{infty}$ filter are also developed. A numerical example is provided to show the effectiveness of the presented method.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115166251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Coating Inspection System for Marine and Offshore Corrosion Management","authors":"Lili Liu, E. Tan, Z. Cai, Yongda Zhen, Xieping Yin","doi":"10.1109/ICARCV.2018.8581327","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581327","url":null,"abstract":"The application of protective coatings is the primary method for protecting marine and offshore structures from coating breakdown and corrosion (CBC). The CBC assessment is a major aspect of coating failure management. Evaluation methods can result in unnecessary maintenance costs and a higher risk of failure. In order to improve efficiency and productivity, the micro-aerial vehicle (MAV) auxiliary automated CBC Evaluation System (A-CAS) is proposed for effective coating failure inspection. Compared to existing manual inspection solutions by surveyors, this method is more suitable for inspecting large areas by means of capturing and analyzing pictures/videos of the target areas. In this paper, a MAV facilitated CBC assessment system implementing deep learning for object recognition has been developed to provide effective CBC assessment for marine and offshore industries. By using active thermography, it is able to identify corrosion behind coatings. This will greatly improve the work efficiency and reliability of coating inspection for surveyors.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115385947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Wavelet Frame Energy-based Segmentation Method for Biomedical Images","authors":"S. Xie, Weimin Huang, Zhongkang Lu, Su Huang","doi":"10.1109/ICARCV.2018.8581163","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581163","url":null,"abstract":"This paper presents a new medical image segmentation method by using wavelet frame energy distribution, which is the sum of squares of the wavelet frame coefficients at each pixel. This work shows that the wavelet frame energy distribution contains the fine texture information extracted from images with low intensity contrast and complex structures using wavelet frame transform. Thus it is employed to enhance the segmentation quality under some challenge conditions such as low intensity contrast, weak/ambiguous boundaries, intensity inhomogeneity and heavy noise. Furthermore, this paper adopts convex relaxation approach to solve the corresponding optimization problem instead of classical level-set method, so the leading numerical computation is efficient and robust to initialization values. Experimental results also illustrate the efficiency of the proposed segmentation method for biomedical images under these extreme imaging conditions.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116239224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}