2007 IEEE Workshop on Advanced Robotics and Its Social Impacts最新文献_第2页

Optimization of workspace of a 2 DOF parallel minirobot using Genetic Algorithms and Simulated Annealing optimization methods 基于遗传算法和模拟退火优化方法的2自由度并联微型机器人工作空间优化

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531418

S. Stan, V. Maties, R. Balan

引用次数: 2

Facial expression recognition under illumination variation 光照变化下的面部表情识别

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531421

Jung-Wei Hong, K. Song

{"title":"Facial expression recognition under illumination variation","authors":"Jung-Wei Hong, K. Song","doi":"10.1109/ARSO.2007.4531421","DOIUrl":"https://doi.org/10.1109/ARSO.2007.4531421","url":null,"abstract":"Extracting facial feature is a key step in facial expression recognition (FER). Inaccurate feature extraction very often results in erroneous categorizing of facial expressions. Especially in robotic application, environmental factors such as illumination variation may cause FER system to extract feature inaccurately. In this paper, we propose a robust facial feature point extraction method to recognize facial expression in various lighting conditions. Before extracting facial features, a face is localized and segmented from a digitized image frame. Face preprocessing stage consists of face normalization and feature region localization steps to extract facial features efficiently. As regions of interest corresponding to relevant features are determined, Gabor jets are applied based on Gabor wavelet transformation to extract the facial points. Gabor jets are more invariable and reliable than gray-level values, which suffer from ambiguity as well as illumination variation while representing local features. Each feature point can be matched by a phase-sensitivity similarity function in the relevant regions of interest. Finally, the feature values are evaluated from the geometric displacement of facial points. After tested using the AR face database and the database built in our lab, average facial expression recognition rates of 84.1% and 81.3% are obtained respectively.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126177697","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}

引用次数: 14

An autonomous surveillance and security robot team 一个自主监视和安全机器人团队

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531425

T.-H.S. Li, Chih-Yang Chen, Ying-Chieh Yeh, C. Yang, Hong-Kai Huang, H. Hung, C. Chu, Shiuan-Hung Hsu, Ding-Yao Huang, Bing-Rung Tsai, Min-Chi Gau, Ren-Jau Jang

{"title":"An autonomous surveillance and security robot team","authors":"T.-H.S. Li, Chih-Yang Chen, Ying-Chieh Yeh, C. Yang, Hong-Kai Huang, H. Hung, C. Chu, Shiuan-Hung Hsu, Ding-Yao Huang, Bing-Rung Tsai, Min-Chi Gau, Ren-Jau Jang","doi":"10.1109/ARSO.2007.4531425","DOIUrl":"https://doi.org/10.1109/ARSO.2007.4531425","url":null,"abstract":"The motivation of this paper is to set up a surveillance and security robot team (SSRT, three autonomous mobile robots and a surveillance and security control (SSC) center. The SSC center communicates with clients via the Internet which can assign patrol scheduler for each robot. The SSRT shares the environmental information among team members through a wireless communication network. The architecture of the SSRT can be divided into two parts. The first one is sensory feedback park. Each surveillance and security robot (SSR) can transmit and receive useful some data, for example, the electric energy residue and the result of patrol, to SSC center via the internet. The other one is mission assignment. The SSC center sorts out the priority of each SSR for the mission execution and assigns each SSR a suitable mission, for example, if any unusual event happens, the SSR will alarm and send the pictures to the SSC center, and then the center will announce the robot employer and assign the optimal patrol role for every robot. The most suitable robot will go to support the incident area, while the other robots will evacuate people to decrease any possible damage. A fuzzy logic decision algorithm is adopted to find the optimal path for each robot to reach the incident location. By combining RFID data with the vision system to recognize the bar-code characteristics in the environment, the self-localization and the patrol routine for each robot can be accomplished. Each robot possesses the auto-recharge function which can recharge itself automatically. Finally, real-time experiments are executed to demonstrate the feasibility and effectiveness of the proposed schemes.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129638019","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}

引用次数: 10

Development of a multisensor-based residual power prediction system for mobile robots 基于多传感器的移动机器人剩余功率预测系统的研制

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531424

K. Su, J. Tzou, C. Liu

{"title":"Development of a multisensor-based residual power prediction system for mobile robots","authors":"K. Su, J. Tzou, C. Liu","doi":"10.1109/ARSO.2007.4531424","DOIUrl":"https://doi.org/10.1109/ARSO.2007.4531424","url":null,"abstract":"Based on the measurements of multiple sensors, this paper presents a residual power detection system for a mobile robot. We use four current sensors to measure the current variety of the mobile robot, and use multilevel multisensor fusion method to detect and diagnosis current sensor and voltage signals status. Moreover, a two level method is used to isolate faulty measured value such that more exact current status to be obtained. In this method, a redundant management method and a statistical prediction method are used in levels one and two, respectively. We use the same method to measure voltage of power system for mobile robots, and isolate faulty measured values. We design the power detection and isolation module using HOLTEK microchip according to the redundant management method. This module can transmit measured value and decision output to main controller (IPC) using series interface (RS232). However, it is possible that this method is faulty. In this case, the IPC can decide an exact power measured value according the statistical signal prediction method. Then we can predict the residual power of mobile robots using polynomial regression algorithm. Finally, we implement the proposed method on the experiment scenario of can set a power threshold value to calculate the critical time for the mobile robot. Meanwhile, experimental results are given to show the feasibility of the proposed method.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126512084","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}

引用次数: 8

Design and application of a new 3-DOF translational parallel manipulator 新型三自由度平移并联机构的设计与应用

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531432

Yangmin Li, Qingsong Xu

引用次数: 8

Design of multi-functional bed for bedridden patient 卧床病人多功能床的设计

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531411

Shih-Wei Peng, Feng‐Li Lian

引用次数: 4

Mobile sensor node localization based on sensor fusion using covariance intersection approach 基于协方差相交法的传感器融合移动传感器节点定位

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531414

R. Luo, Sung-Sheng Huang, Wei-Lung Hsu

引用次数: 1

A comparative study of implementing Fast Marching Method and A* SEARCH for mobile robot path planning in grid environment: Effect of map resolution 网格环境下移动机器人路径规划快速行军法与A* SEARCH的比较研究:地图分辨率的影响

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531429

C. H. Chiang, Po Jui Chiang, J.C.-C. Fei, J. Liu

引用次数: 31

Indoor mobile robot localization using probabilistic multi-sensor fusion 基于概率多传感器融合的室内移动机器人定位

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531415

R. Luo, Chun C. Lai

引用次数: 7

Fuzzy hybrid navigation of an active mobile robotic assistant: A multisensory fusion approach 主动移动机器人助手的模糊混合导航:一种多感官融合方法

2007 IEEE Workshop on Advanced Robotics and Its Social Impacts Pub Date : 2007-12-01 DOI: 10.1109/ARSO.2007.4531423

Ching-Chih Tsai, Ssu-Min Hu, Hsu-Chih Huang, Shih-Min Hsieh

引用次数: 4