Self-organising fuzzy decision trees for robot navigation: An on-line learning approach

被引:0
|
作者
Hamzei, GHS [1 ]
Mulvaney, DJ [1 ]
机构
[1] Loughborough Univ Technol, Dept Elect & Elect Engn, Loughborough LE11 3TU, Leics, England
来源
1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5 | 1998年
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a new hybrid technique for intelligent robot navigation based on incremental decision trees (ITI-2.8) and incorporating fuzzy logic for flexible control. The robot perception is decomposed into a hierarchy of simpler virtual environments, termed worlds. Training examples generated from the robot's past rewarded experiences are exposed to ITI-2.8 in an incremental manner and on-line to evolve an array of Fuzzy Associative Memories (FAM), each representing a unique world. That is, generated FAMs, which are structurally nonlinear (in contrast to ordinary FAMs), are engineered on-line and from inception to store and access fuzzy control rule spaces representing different perceptions. Each decision tree is encoded In one FAM and is local to a certain perception. The fundamental strengths of the algorithm in building on-line FAMs, is its incremental nature and automatically generating fuzzy training vectors without human intervention. Fuzziness is integrated to provide suitable reasoning in the face of inherent uncertainty in the sensory input data and to merge conflicting behaviours to generate smooth trajectories. Global navigation is achieved by activating a hierarchy of local FAMs.
引用
收藏
页码:2332 / 2337
页数:6
相关论文
共 50 条
  • [41] A Self-organising Approach for Smart Meter Communication Systems
    Tauber, Markus Gerhard
    Skopik, Florian
    Bleier, Thomas
    Hutchison, David
    SELF-ORGANIZING SYSTEMS: 7TH IFIP TC 6 INTERNATIONAL WORKSHOP (IWSOS 2013), 2014, 8221 : 169 - 175
  • [42] Self-Organising News Management: The Molecules of Knowledge Approach
    Mariani, Stefano
    Omicini, Andrea
    2012 IEEE SIXTH INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS (SASOW), 2012, : 235 - 240
  • [43] A hippocampal-inspired self-organising learning memory model with analogical reasoning for decision support
    Tung, W. L.
    Quek, C.
    2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2006, : 1154 - +
  • [44] Decentralised Self-Organising Maps for Multi-Robot Information Gathering
    Best, Graeme
    Hollinger, Geoffrey A.
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 4790 - 4797
  • [45] Self-Organising and Self-Learning Model for Soybean Yield Prediction
    Alghamdi, Mona
    Angelov, Plamen
    Gimenez, Raul
    Rufino, Mariana
    Soares, Eduardo
    2019 SIXTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), 2019, : 441 - 446
  • [46] Unsupervised on-line learning of decision trees for hierarchical data analysis
    Held, M
    Buhmann, JM
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 10, 1998, 10 : 514 - 520
  • [47] Improved performance of self-organising fuzzy controller (SOC) in pH control
    Ylen, JP
    1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2, 1998, : 258 - 263
  • [48] Limits to self-organising systems of learning-the Kalikuppam experiment
    Mitra, Sugata
    Dangwal, Ritu
    BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 2010, 41 (05) : 672 - 688
  • [49] A Drowsiness Detection Decision Support System using Self-Organising Map
    Emmanuel, Fabunmi Temitayo
    Ojo, Adedayo Olukayode
    Gbadamosi, Saheed Lekan
    2022 IEEE NIGERIA 4TH INTERNATIONAL CONFERENCE ON DISRUPTIVE TECHNOLOGIES FOR SUSTAINABLE DEVELOPMENT (IEEE NIGERCON), 2022, : 307 - 310
  • [50] Local optimality of self-organising neuro-fuzzy inference systems
    Gu, Xiaowei
    Angelov, Plamen
    Rong, Hai-Jun
    INFORMATION SCIENCES, 2019, 503 : 351 - 380