Art
J-GLOBAL ID:202402234715437359   Reference number:24A0213153

CLSQL: Improved Q-Learning Algorithm Based on Continuous Local Search Policy for Mobile Robot Path Planning

CLSQL:移動ロボット経路計画のための連続局所探索ポリシーに基づく改良Q学習アルゴリズム【JST・京大機械翻訳】
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Material:
Volume: 22  Issue: 15  Page: 5910  Publication year: 2022 
JST Material Number: U7015A  ISSN: 1424-8220  CODEN: SENSC9  Document type: Article
Article type: 原著論文  Country of issue: Switzerland (CHE)  Language: ENGLISH (EN)
Abstract/Point:
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Design,manufacturing,and components of robots  ,  Motion and control of robots  ,  Measuring instruments in general 
Reference (34):
  • Sherwani, F.; Asad, M.M.; Ibrahim, B.S.K.K. Collaborative robots and industrial revolution 4.0 (IR 4.0). In Proceedings of the 2020 International Conference on Emerging Trends in Smart Technologies (ICETST), Karachi, Pakistan, 26-27 March 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 1-5.
  • Bai, X.; Yan, W.; Cao, M.; Xue, D. Distributed multi-vehicle task assignment in a time-invariant drift field with obstacles. IET Control Theory Appl. 2019, 13, 2886-2893.
  • Hart, P.E.; Nilsson, N.J.; Raphael, B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 1968, 4, 100-107.
  • LaValle, S.M. Rapidly-Exploring Random Trees: A New Tool for Path Planning; Iowa State University: Ames, IA, USA, 1998.
  • Grefenstette, J.J. Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man. Cybern. 1986, 16, 122-128.
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