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  1. 学位論文
  2. 学位論文

採餌問題のための確率的探索戦略の設計と最適化

https://doi.org/10.18997/00007483
https://doi.org/10.18997/00007483
9d92a24d-61ba-4ef8-8a61-1d2d6183c552
名前 / ファイル ライセンス アクション
sei_k_358.pdf sei_k_358.pdf (3.3 MB)
アイテムタイプ 学位論文 = Thesis or Dissertation(1)
公開日 2019-12-10
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_db06
資源タイプ doctoral thesis
タイトル
タイトル Design and Optimization of Stochastic Search Strategy for Foraging Problem
言語 en
タイトル
タイトル 採餌問題のための確率的探索戦略の設計と最適化
言語 ja
言語
言語 eng
著者 Maroofkhani, Farhad

× Maroofkhani, Farhad

en Maroofkhani, Farhad

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内容記述タイプ Abstract
内容記述 Autonomous robot’s search strategy is the set of rules that it employs while looking for targets in its environment. In this study, the stochastic movement of robots in unknown environments is statistically studied, using a Levy walk method. Biological systems (e.g., foraging animals) provide useful models for designing optimal stochastic search algorithms. Observations of biological systems, ranging from large animals to immune cells, have inspired the design of efficient search strategies that incorporate stochastic movement. In this study, we seek to identify the optimal stochastic strategies for autonomous robots. Given the complexity of interaction between the robot and its environment, optimization must be performed in high-dimensional parameter space. The effect of the explanatory variable on the forger robot movement with the minimum required energy was also studied using experiments done by the response surface methodology (RSM). We analyzed the extent to which search efficiency requires these characteristics, using RSM. Correlation between the involved parameters via a Lévy walk process was examined through designing a setup for the experiments to determine the interaction of the involved variables and the robot movement. The extracted statistical model represents the priority influence of those variables on the robot by developing the statistical model of the mentioned unknown area. The efficiency of a simple strategy was investigated based on Lévy walk search in two-dimensional landscapes with clumped resource distributions. We show how RSM techniques can be used to identify optimal parameter values as well as to describe how sensitive efficiency reacts to the changes in these values. Here, we identified optimal parameter for designing robot by using stochastic search pattern and applying mood-switching criteria on a mixture of speed and sensor and μ to determine how many robots are needed for a solution. Fractal criterion-based robot strategies were more efficient than those based on the resource encounter criterion, and the former was found to be more robust to changes in resource distribution as well.
言語 en
目次
内容記述タイプ TableOfContents
内容記述 1 Introduction||2 Levy Walk||3 Design of Experiment (DOE)||4 Response Surface Methodology||5 Results and Discussions||6 Conclusion
備考
内容記述タイプ Other
内容記述 九州工業大学博士学位論文 学位記番号:生工博甲第358号 学位授与年月日:令和元年9月20日
キーワード
主題Scheme Other
主題 Autonomous Robots
キーワード
主題Scheme Other
主題 Biomimetic
キーワード
主題Scheme Other
主題 Biologically-Inspired
キーワード
主題Scheme Other
主題 Stochastic Search
キーワード
主題Scheme Other
主題 Optimal Foraging
アドバイザー
石井, 和男
学位授与番号
学位授与番号 甲第358号
学位名
学位名 博士(工学)
学位授与年月日
学位授与年月日 2019-09-20
学位授与機関
学位授与機関識別子Scheme kakenhi
学位授与機関識別子 17104
学位授与機関名 九州工業大学
学位授与年度
内容記述タイプ Other
内容記述 令和元年度
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
ID登録
ID登録 10.18997/00007483
ID登録タイプ JaLC
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