2007 International Symposium on Computational Intelligence in Robotics and Automation最新文献_第8页

Tree Measurement in Forest by 2D Laser Scanning 二维激光扫描测量森林树木

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382858

Jaakko Jutila, Kosti Kannas, A. Visala

引用次数: 51

Evaluation of a Parallel Architecture and Algorithm for Mapping and Localization 一种用于映射和定位的并行架构及算法的评估

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382895

D. Lyons, Giselle R. Isner

引用次数: 2

Using Heuristic Search To Find Stable High-Order Single-Bit Delta Sigma Modulators 用启发式搜索寻找稳定的高阶单比特δ σ调制器

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382866

S. Skogstad, M. Høvin

引用次数: 1

Algebraic interval constraint driven exploration in human-agent collaborative problem solving 人-智能体协同问题求解中的代数区间约束探索

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382872

Yan Wang

引用次数: 3

Micro Planar Parallel Mechanism Design using Configuration Change Effect of Flexure Mechanism 基于柔性机构构形变化效应的微平面并联机构设计

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382899

B. Kang, K. R. Cho

引用次数: 0

A Problem Solving Environment for Combinatorial Optimization Based on Parallel Meta-heuristics 基于并行元启发式的组合优化问题求解环境

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382922

Rong Huang, Shurong Tong, W. Sheng, Zhun Fan

引用次数: 1

Potentially Distributable Energy: Towards Energy Autonomy in Large Population of Mobile Robots 潜在的可分配能量:迈向大规模移动机器人的能量自治

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382875

T. Ngo, Hector Raposo, H. Schiøler

{"title":"Potentially Distributable Energy: Towards Energy Autonomy in Large Population of Mobile Robots","authors":"T. Ngo, Hector Raposo, H. Schiøler","doi":"10.1109/CIRA.2007.382875","DOIUrl":"https://doi.org/10.1109/CIRA.2007.382875","url":null,"abstract":"We propose a new concept, potentially distributable energy, in the field of autonomous mobile robots. Considering a system of multiple robots powered by batteries, each robot no longer works since its energy capacity is expired. To extend operating time, the robot needs to replenish energy through recharging energy. To date, except research on vaccum cleaning robots that are able to recharge battery when docking with the fixed station has been achieved, there does not exist any research of energy distribution to prolong operating time in a large population of mobile robots. This could be caused by the lack of using rechargeable battery in the fact that the robot has to normally spend the charging-time much longer than the operating time. This paper presents simulation results of mobile robots that are capable of not only self-recharging energy but also exchanging batteries to the other robots. Initially, we describe a simulation of multiple mobile robots, and then issue rules of battery exchange, which is formulated under constraints of workload, distance and remaining capacity. The simulation shows that: (a) a robot is able to be energetically autonomous if its energy can be replenished by the other robots or it is able to come back to the main charging station to replenish itself; (b) energy of a robot is always under constraints of energy distribution of the mother charging station and the other robots; (c) distributed energy balance is the main elements to decide a number of robots in a specific area and an ideal location of the repository when one wants to deploy a large population of autonomous mobile robots. Finally, based on results of the simulation we adjust rules for our real multirobot system.","PeriodicalId":301626,"journal":{"name":"2007 International Symposium on Computational Intelligence in Robotics and Automation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131480831","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: 23

New Results in Understanding Performance and Vulnerability in Complex Networks 理解复杂网络性能和脆弱性的新成果

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382833

D. Repperger

引用次数: 0

Self Organized Networks for Optimal Feature Extraction 自组织网络的最优特征提取

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382908

Y. A. Ghassabeh, H. Moghaddam

{"title":"Self Organized Networks for Optimal Feature Extraction","authors":"Y. A. Ghassabeh, H. Moghaddam","doi":"10.1109/CIRA.2007.382908","DOIUrl":"https://doi.org/10.1109/CIRA.2007.382908","url":null,"abstract":"In this paper, we introduced new adaptive learning algorithms and related networks to extract optimal features from multidimensional data in order to reduce the data dimensionality while preserving class separability. For this purpose, new adaptive algorithms for the computation of the square root of the inverse covariance matrix Sigma-1/2 are introduced. We introduce a new cost function related to the given adaptive learning algorithms in order to prove their convergence. Self organized Sigma-1/2 networks are constructed based on these algorithms. By cascading Sigma-1/2 network and an adaptive principal component analysis (APCA) network, we present new adaptive self organized LDA feature extraction network. Adaptive nature of the new optimal feature extraction method makes it appropriate for on-line incremental pattern classification and machine learning applications. Both networks in the proposed structure are trained simultaneously, using a stream of input data. Existence of cost function, make it available to compute learning rate efficiently in every iteration in order to increase the convergence rate. Experimental results using synthetic multi-class multi-dimensional sequence of data, demonstrated the effectiveness of the new adaptive self organized feature extraction networks.","PeriodicalId":301626,"journal":{"name":"2007 International Symposium on Computational Intelligence in Robotics and Automation","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126658790","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: 1

The 2005 DARPA Grand Challenge 2005年DARPA大挑战

2007 International Symposium on Computational Intelligence in Robotics and Automation Pub Date : 2007-06-20 DOI: 10.1109/CIRA.2007.382834

C. Crane

引用次数: 26