Jin-Il Kang, Hyeung-Sik Choi, B. Jun, Ngoc-Duc Nguyen, Joon-Young Kim
{"title":"Control and implementation of underwater vehicle manipulator system using zero moment point","authors":"Jin-Il Kang, Hyeung-Sik Choi, B. Jun, Ngoc-Duc Nguyen, Joon-Young Kim","doi":"10.1109/UT.2017.7890291","DOIUrl":"https://doi.org/10.1109/UT.2017.7890291","url":null,"abstract":"Underwater Vehicle-Manipulator System (UVMS) is a useful system to perform diverse and sophisticated operations under the water. However it is difficult to control the motion of UVMS due to external disturbances such as payloads holding underwater objects, currents, etc. In this paper, in order to ensure the dynamic stability of the UVMS, redundancy resolution method is proposed using a zero moment point (ZMP) algorithm. In order to evaluate the proposed ZMP algorithm and redundancy resolution method, a testbed composed of redundant manipulator was developed. The results show that ZMP algorithm helps the stability of the UVMS while the end-effector of the manipulator tracks the desired trajectory accurately.","PeriodicalId":145963,"journal":{"name":"2017 IEEE Underwater Technology (UT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124365427","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":"Performance evaluation of safety sensors in the indoor fog chamber","authors":"B. Kim, Y. Sumi","doi":"10.1109/UT.2017.7890317","DOIUrl":"https://doi.org/10.1109/UT.2017.7890317","url":null,"abstract":"In this study, the visibility performance test of safety sensors is carried out in the indoor fog chamber and the effect of the concentration of the fog on the visibility of safety sensors is evaluated. First, safety sensors and outdoor environmental requirements are explained. And also, the relationship between the visibility and the performance of safety sensors is discussed and the current problems for evaluating visibility performance are described. Next, the indoor fog chamber is introduced and spectral transmittance measuring equipment for evaluating the visibility reduction is described. Finally, the use conditions of safety sensors in the outdoors are discussed by measuring the reduction of visibility due to fog and performing preliminary experiments on visibility and sensor performance.","PeriodicalId":145963,"journal":{"name":"2017 IEEE Underwater Technology (UT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115211309","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}
Guoqing Ding, Yan Song, Jia Guo, Chen Feng, Guangliang Li, T. Yan, B. He
{"title":"Side-scan sonar image segmentation using Kernel-based Extreme Learning Machine","authors":"Guoqing Ding, Yan Song, Jia Guo, Chen Feng, Guangliang Li, T. Yan, B. He","doi":"10.1109/UT.2017.7890294","DOIUrl":"https://doi.org/10.1109/UT.2017.7890294","url":null,"abstract":"Autonomous Underwater Vehicles (AUVs) are important platform for oceanographic survey. AUVs have been widely applied to many fields, such as the ocean research, oil and gas exploitation, mineral resources investigation, fishing and military. People can obtain important ocean information by segmenting, classifying and recognizing sonar image of AUV. So studying side-scan sonar image is significant. Markov Random Field (MRF) is an efficient method for segmentation of side-scan sonar image. However, MRF may not work well for side-scan sonar image obtained from complex environment. In these images, pixel values do not change obviously. In this paper, an innovative segmentation method based MRF and Kernel-based Extreme Learning Machine (K-ELM) is proposed for real side-scan sonar image segmentation. This method has been validated on the real sonar images. Experimental results demonstrate that the proposed method outperforms MRF in classification accuracy.","PeriodicalId":145963,"journal":{"name":"2017 IEEE Underwater Technology (UT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121695267","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}
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