Multi-Modal Task Apportionment in Dynamic Multi-Factor Systems

被引:2
|
作者
Samani, Hooman A. [1 ]
Fua, C. [1 ]
Chai, T. [2 ]
Ge, S. S. [1 ]
Hang, C. C. [1 ]
机构
[1] Natl Univ Singapore, Social Robot Lab, Interact Digital Media Inst, Singapore 117576, Singapore
[2] Northeastern Univ, Minist Educ, Res Ctr Automat, Key Lab Proc, Shenyang, Peoples R China
关键词
Task Apportionment; Multi Agent; Production Planning and Scheduling; Dynamic Resource Allocation;
D O I
10.1109/CCDC.2008.4597608
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The problem of task apportionment for dynamic multi-factor systems is categorized in three different scenarios in this paper: Admin, Crew and Factor Based. The system is discussed based on the realistic manufacturing environments as an application platform in order to consider the robust Task Apportionment system for such a case but the proposed system can be generalized to any similar system. Both centralized and decentralized systems are considered and task Allocation, Re-allocation, Exchange and Execution as layers of Task Apportionment methods are designed and described in each system. The system may switch between modes according the feedback information of communication capabilities or uses different modes for each part of the platform.
引用
收藏
页码:1692 / 1697
页数:6
相关论文
共 50 条
  • [1] Task allocation in robot systems with multi-modal capabilities
    Hojda, Maciej
    [J]. IFAC PAPERSONLINE, 2015, 48 (03): : 2109 - 2114
  • [2] Multi-Task and Multi-Modal Learning for RGB Dynamic Gesture Recognition
    Fan, Dinghao
    Lu, Hengjie
    Xu, Shugong
    Cao, Shan
    [J]. IEEE SENSORS JOURNAL, 2021, 21 (23) : 27026 - 27036
  • [3] ZEMFA: Zero-Effort Multi-Factor Authentication based on Multi-Modal Gait Biometrics
    Shrestha, Babins
    Mohamed, Manar
    Saxena, Nitesh
    [J]. 2019 17TH INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST), 2019, : 170 - 179
  • [4] Intelligent multi-modal systems
    Tsui, KC
    Azvine, B
    Djian, D
    Voudouris, C
    Xu, LQ
    [J]. BT TECHNOLOGY JOURNAL, 1998, 16 (03): : 134 - 144
  • [5] MultiMAE: Multi-modal Multi-task Masked Autoencoders
    Bachmann, Roman
    Mizrahi, David
    Atanov, Andrei
    Zamir, Amir
    [J]. COMPUTER VISION, ECCV 2022, PT XXXVII, 2022, 13697 : 348 - 367
  • [6] Multi-modal microblog classification via multi-task learning
    Sicheng Zhao
    Hongxun Yao
    Sendong Zhao
    Xuesong Jiang
    Xiaolei Jiang
    [J]. Multimedia Tools and Applications, 2016, 75 : 8921 - 8938
  • [7] MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving
    Chowdhuri, Sauhaarda
    Pankaj, Tushar
    Zipser, Karl
    [J]. 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2019, : 1496 - 1504
  • [8] Dynamic Multi-factor Authentication for Smartphone
    Yohan, Alexander
    Lo, Nai-Wei
    Lie, Henry Roes
    [J]. 2016 IEEE 27TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2016, : 2448 - 2453
  • [9] Multi-modal multi-task feature fusion for RGBT tracking
    Cai, Yujue
    Sui, Xiubao
    Gu, Guohua
    [J]. INFORMATION FUSION, 2023, 97
  • [10] Multi-modal control of systems with constraints
    Koo, TJ
    Pappas, GJ
    Sastry, S
    [J]. PROCEEDINGS OF THE 40TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5, 2001, : 2075 - 2080