使用人工蜂群演算法和快速搜索隨機樹改進路徑規劃系統之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：61

、訪客IP：18.191.154.174

姓名

崔嶽(Yue Tusi) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

使用人工蜂群演算法和快速搜索隨機樹改進路徑規劃系統之研究
(Using ABC and RRT Algorithms to Improve Path Planning)

相關論文

★ 影像處理運用於家庭防盜保全之研究	★ 適用區域範圍之指紋辨識系統設計與實現
★ 頭部姿勢辨識應用於游標與機器人之控制	★ 應用快速擴展隨機樹和人工魚群演算法及危險度於路徑規劃
★ 智慧型機器人定位與控制之研究	★ 基於人工蜂群演算法之物件追蹤研究
★ 即時人臉偵測、姿態辨識與追蹤系統實現於複雜環境	★ 基於環型對稱賈柏濾波器及SVM之人臉識別系統
★ 改良凝聚式階層演算法及改良色彩空間影像技術於無線監控自走車之路徑追蹤	★ 模糊類神經網路於六足機器人沿牆控制與步態動作及姿態平衡之應用
★ 四軸飛行器之偵測應用及其無線充電系統之探討	★ 結合白區塊視網膜皮層理論與改良暗通道先驗之單張影像除霧
★ 基於深度神經網路的手勢辨識研究	★ 人體姿勢矯正項鍊配載影像辨識自動校準及手機接收警告系統
★ 模糊控制與灰色預測應用於隧道型機械手臂之分析	★ 模糊滑動模態控制器之設計及應用於非線性系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本研究以人工蜂群演算法(ABC)結合快速搜索隨機樹(RRT)來創新路徑規劃演算法應用在移動式機器人當中，路徑規劃對於移動機器人是非常重要的研究之一，本文目的是如何在有障礙物的環境中，規劃出一條適合行走且無危險和有效率的路徑，讓機器人從起始點移動到目標點是安全且正確的。
與傳統的演算法有所不同，本文是先用RRT演算法的方式來尋找延伸點，在數個延伸點經過我們比較之後，選擇最佳的延伸點來使蜜蜂移動，因為人工蜂群演算法擁有結構簡單、容易操作且收斂速度較快的性能，他改善了以往用於路徑規劃的演算法在收斂速度慢和容易陷入區域最佳點的問題，雖然RRT在搜索未知區域方面有優良的特性，但它在每次規劃路徑上是不穩定的，所以本文結合人工蜂群的特性加入到RRT的演算法上，並處理障礙物的問題來模擬機器人的路徑。
總而言之，本文提出改良的演算法較以往單獨的RRT或單獨的人工蜂群演算法更具有效率與穩定且路徑最短。

摘要(英)

In this study, we use the Artificial Bee Colony algorithm incorporating the fast search Random Tree (RRT) for innovation path planning, and is implemented in a mobile robot. Path planning for mobile robot is one of the very important researches. The aim is how to plan a path in the environment which have obstacles, and make the robot walk from start point to target safely and correctly.
Different from conventional algorithms, we use the RRT algorithm to find several extending points, and after comparison we choose the best extending point to make the extension of bees move. Because Artificial Bee Colony algorithm is simple structure, easy operation and fast convergence, it improved the problem of path planning which in the slow convergence and easy to local optimal solution in previous path planning algorithms. While the RRT has excellent characteristics in seeking unknown area, it is unstable for each planning. We herein combine the characteristics of artificial bee colony with the RRT algorithms to deal with the problem of obstacles and make practical simulation in a mobile robot.
In summary, it is shown that the data of the new algorithm on path planning are much more effective and stable than those of either single ABC or single RRT, and the path is the shortest.

關鍵字(中)

★ 人工蜂群演算法
★ 快速搜索隨機樹
★ 移動機器人
★ 路徑規劃
★ 危險度

關鍵字(英)

★ Artificial bee colony
★ Rapidly-exploring Random Tree
★ Path Planning
★ Mobile Robot
★ Danger Degree Map

論文目次

目錄
中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
第一章緒論 1
1-1 簡介 1
1-2 研究背景 2
1-3 文獻探討 3
1-4成果與主要貢獻 5
1-5論文架構 5
第二章路徑規劃的問題定義與敘述 6
2-1定義路徑規劃 6
2-2路徑規劃的種類 7
2-3全域路徑規劃方法 7
2-3-1 Dijkstra’s演算法 8
2-3-2 A*演算法 9
2-3-3 拓樸法 10
2-3-4 可視圖法(visibility graph) 11
2-3-5 柵欄法 13
2-4 局部規劃路徑方法 14
2-4-1模擬退火法 14
2-4-2人工勢場法 14
2-4-3模糊控制算法 15
2-4-4類神經網路 16
2-5 本篇論文的環境設定 16
第三章本篇論文使用的方法與問題分析 22
3-1 快速擴展隨機樹演算法(RRT) 22
3-2 雙向快速搜尋隨機樹 26
3-3 人工蜂群演算法(ABC) 28
3-3-1 人工蜂群演算法背景 28
3-3-2 人工蜂群的行為模式 28
3-3-3 人工蜂群的數學模式 31
3-3-4 人工蜂群演算法的程序 33
3-3-5 人工蜂群演算法的行為和參數設置 36
3-4 障礙物的危機處理 37
第四章路徑規劃的改進 39
4-1改進方法(I) 40
4-1-1理念架構 40
4-1-2適應函數與懲罰值的問題 44
4-2 改進方法(II) 48
4-2-1演算法如何選擇延伸點 48
4-2-2改進方法(II)的流程 49
4-3改進延伸點的選擇方式 55
4-4改良人工蜂群演算法加上快速搜索擴展隨機樹算法步驟 61
第五章實驗結果與討論 65
5-1演算法和地圖介紹 65
5-2 ABC混合型演算法細節討論 69
5-2-1 蜜蜂的數目和疊代次數的關係 69
5-2-2延伸點的設定 70
5-3演算法在簡單地圖中的情形 72
簡單地圖 73
5-4 演算法在複雜地圖上的情形 77
複雜地圖1 78
複雜地圖2 83
5-5危險度機制的加入 87
5-6動態環境測試 89
第六章結論 92
6-1總結 92
6-2未來建議 93
參考文獻 94

圖目錄
圖1-1 Dijkstra演算法和A*演算法比較圖 4
圖2-1 Dijkstra’s演算法擴散圖 8
圖2-2 有向圖 9
圖2-3 A*搜尋演算法示意圖 10
圖2-4 拓樸法建立拓樸網路示意圖 11
圖2-5 visibility graph示意圖 12
圖2-6 Voronoi 圖法示意圖 13
圖2-7 人工位能場所規劃的路徑 15
圖2-8 欄格環境 18
圖2-9 結構圖 19
圖2-10 本文所設定的簡單環境 20
圖3-1 基本RRT生長圖 23
圖3-2 RRT數據結構 25
圖3-3 雙向擴展的RRT示意圖 27
圖3-4 蜜蜂的覓食行為 31
圖3-5 人工蜂群演算法流程圖 35
圖3-6 加入安全領域和之前地圖的比較 37
圖3-7 危險度地圖 38
圖4-1 在不同疊代次數下經過改進方法(I)的示意圖 42
圖1-2 兩點間障礙物的判斷方向 45
圖4-3 改進方法(II)中延伸點的選擇方式示意圖 49
圖4-4 新的延伸點產生範圍示意圖 52
圖4-5 延伸點取樣範圍隨著疊代次數增加而縮小曲線圖 52
圖4-6 改進方法(II)的流程圖 53
圖4-7 延伸點延伸時可能發生的情況 55
圖4-8 加入新方法的延伸點選擇示意圖 58
圖4-9 陷入局部最佳解的示意圖 59
圖4-10 延伸點的可能擴展方向 60
圖4-11 人工蜂群演算法加上RRT流程圖 63
圖5-1 程式介面 65
圖5-2 簡單地圖環境 66
圖5-3 複雜地圖環境1 67
圖5-4 複雜地圖環境2 68
圖5-5 簡單地圖中ABC在不同疊代次數下模擬圖 73
圖5-6 簡單地圖中AFSA在不同疊代次數下模擬圖 74
圖5-7 簡單地圖中HPSO-TVAC在不同疊代次數下模擬圖 75
圖5-8 簡單地圖中PSO在不同疊代次數下模擬圖 76
圖5-9 複雜地圖1中PSO在不同疊代次數下模擬圖 78
圖5-10 複雜地圖1中HPSO-TVAC在不同疊代次數下模擬圖 79
圖5-11 複雜地圖1中AFSA在不同疊代次數下模擬圖 80
圖5-12 複雜地圖1中ABC在不同疊代次數下模擬圖 81
圖5-13 複雜地圖2中PSO在不同疊代次數下模擬圖 83
圖5-14 複雜地圖2中HPSO-TVAC在不同疊代次數下模擬圖 84
圖5-15 複雜地圖2中AFSA在不同疊代次數下模擬圖 85
圖5-16 複雜地圖2中ABC在不同疊代次數下模擬圖 86
圖5-17 在複雜地圖1中加入危險度的ABC演算法比較圖 88
圖5-18 在複雜地圖2中加入危險度的ABC演算法比較圖 89
圖5-19 動態環境地圖 90
圖5-20 不同疊代次數在動態環境下的模擬圖 91

表目錄
表3-1 傳統RRT演算法的pseudo code 26
表4-1 改進疊代次數的執行函式 43
表4-2 懲罰值執行函式 47
表5-1 路經規劃演算法參數 69
表5-2 蜜蜂數目和疊代時間對照表 70
表5-3 有無固定延伸點的對照表 70
表5-4 延伸點的產生與疊代次數關係表 71
表5-5 四種演算法在簡單地圖上的比較 77
表5-6 四種演算法在複雜地圖1上的比較 82
表5-7 四種演算法在複雜地圖2上的比較 87

參考文獻

參考文獻
[1] Q. Wu and B. Zeng, “Real-time globally optimized path planning in a dynamic environment,” IEEE International Conference on Intelligent Computation Technology and Automation, Vol.3, pp. 261-264, 2009.
[2] X. Ye, Y. Lei and H. Hon, “Design of intelligent mobile vehicle system and its global optimal path planning,” IEEE International Conference on Industrial Technology, pp. 1-5,21-24, 2008.
[3] A. T. Klesh, P. T. Kabamba and A.R. Girard, “Optimal Path planning for uncertain exploration,” American Control Conference, pp. 2421-2426, 2009.
[4] S. L. Valle, J. Kuffner. “Rapidly-exploring random trees: Progress and Prospects,” In Proceedings of Algorithmic and Computational Robotics: New Directions, pp. 293-308, 2001.
[5] M.S.Ganeshmurthy and Dr.G.R.Suresh, “Path Planning Algorithm for Autonomous Mobile Robot in Dynamic Environment” Signal Processing, Communication and Networking (ICSCN), 2015 3rd International Conference on, pp. 1-6, 2015.
[6] Jitin Kumar Goyal and K.S.Nagla, “A New Approach of Path Planning for Mobile Robots” Advances in Computing, Communications and Informatics ICACCI, 2014 International Conference on , pp.863-867, 2014.
[7] Meijuan Gao and Jingwen Tian, “Path Planning for Mobile Robot Based on Improved Simulated Annealing Artificial Neural Network Third International Conference on Natural Computation (ICNC 2007) , pp. 8-12, 2007.
[8] J. Kennedy and R. Eberhart, "Particle Swarm Optimization," In Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
[9] Q. Mal and X. Lei, “Application of Artificial Fish School Algorithm in UCAV Path Planning”, IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), pp.555-559, 2010.
[10] D. Karaboga, “An Idea Based On Honey Bee Swarm for Numerical Optimization,” Technical Report TR06, Computer Engineering Department, Erciyes University, 2005.
[11] Yannis Marinakis , “A hybrid discrete Artificial Bee Colony-Grasp algorithm for clustering” Computers & Industrial Engineering, 2009. CIE 2009. International Conference on, pp. 548-553, 2009.
[12] K. Sugihara and J. Smith, “Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot,” In Proceedings of IEEE international Symposium on Computational Intelligence in Robotics and Automation, pp. 138-143, 1997.
[13] I. Ashiru, C. Czarnecki, and T. Routen, “Characteristics of a Genetic Based Approach to Path Planning for Mobile Robots,” J. Network and Computer Applications, Vol. 19, pp. 149-169, 1996.
[14] A. Coloni, M. Dorigo, V. Maniezzo, et a1. Distributed optimization by ant colonies. In Proceedimgs of the 1st European Confercnce on Artificial Life, pp. 134-142, 1991.
[15] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik 1, pp. 269-271, 1959.
[16] P. E. Hart, N. J. Nilsson, B. Raphael, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths," IEEE Transactions on Systems Science and Cybernetics SSC4 4 (2), pp. 100-107, 1968.
[17] P. E. Hart, N. J. Nilsson, B. Raphael, “Correction to A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” SIGART Newsletter 37, pp. 28-29, 1972.
[18] R. Dechter, P. Judea, “Generalized best-first search strategies and the optimality of A*,” Journal of the ACM 32 (3), pp. 505-536, 1985.
[19] http://theory.stanford.edu/~amitp/GameProgramming
[20] D. Fox, W. Burgard, and S. Thrun, “Markov localization for mobile
robots in dynamic environments,” Journal of Artificial Intelligence Research, Vol. 11, pp. 391-427, 1999.
[21] F. Dellaert, D. Fox, W. Burgard and S. Thrun, “Monte Carlo localization for mobile robots,” IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1322-1328, 1999.
[22] Yujang Yi , “A Novel Artificial Bee Colony Algorithm” Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on, pp. 271-274, 2014.
[23] S. Thrun, D. Fox, W. Burgard and F. Dallaert, “Robust Monte Carlo Localization for Mobile Robots,” Artificial Intelligence, Vol. 128, pp. 99-141, 2001.
[24] M. B. Metea. “Planning and intelligence autonomous land vehicles using hierarchical terrain representation,” In Proceedings of IEEE International Conference on Robotics and Automation, pp. 1947-1952, 1987.
[25] E. Hernandez, M. Carreras, J. Anitich, P. Ridao, and A. Ortiz, “A topologically guided path planner for an AUV using homotopy classes,” 2011 IEEE International Conference on Robotics and Automation, pp.2337-2343, 2011.
[26] J. C. Latombe, Robot Motion Planning. Norwood, MA, Kluwer Academic Publishers, 1991.
[27] J. A. Janet, R. C. Luo, M. G. Kay, “The essential visibility graph: an approach to global motion planning for autonomous mobile robots,” IEEE International Conference on Robotics and Automation, Vol. 2, pp. 21-27, 1995.
[28] W. Adiprawita, A.S. Ahmad, J. Sembiring and B.R. Trilaksono, “New resampling algorithm for particle filter localization for mobile robot with 3 ultrasonic sonar sensor,” International Conference on Electrical Engineering and Informatics, pp.1-6, 2011.
[29] W. Hong, C. Zhou and Y. Tian, “Robust Monte Carlo Localization for humanoid soccer robot”, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp.934-939, 2009.
[30] Tarun Kumar Sharma , “Improved food sources in Artificial Bee Colony” Swarm Intelligence (SIS), 2013 IEEE Symposium on, pp. 95-102, 2013.
[31] O. Takahashi, R. J. Schilling. “Motion Planning in a plane using gene-realized voronoi diagrams,” IEEE Trans Robotics and Automation, pp.143-150, 1989.
[32] Khatib, “Real-time obstacle for manipulators and mobile robot,” International Journal of Robotic Research, pp. 90-98, 1986.
[33] M. A. Goodrich, Potential Fields Tutorial, 2002. Available at：http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.115.3259&rep=rep1&type=pdf
[34] Garrison W. Greenwood, “A modified artificial bee colony algorithm for solving large graph theory problems” IEEE Congress on Evolutionary Computation, pp. 713-717, 2013.
[35] Xiaohu Shi , “An integrated algorithm based on artificial bee colony and particle swarm optimization” 2010 Sixth International Conference on Natural Computation ,pp. 2586-2590, 2010.
[36] N. H. C. Yung and Y. Cang, “An Intelligent Mobile Vehicle Navigator Based on Fuzzy Logic and Reinforcement Learning,” IEEE Trans on Sys Man and Cybern. Part B: Cyberneics, pp, 314-320, 1999.
[37] Wei-feng Gao, San-yang Liu, and Ling-ling Huang, “A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning” IEEE Transactions on Cybernetics ,pp. 1011-1024, 2013.
[38] W Li, G. Y. Wang. “Application of improved PSO in mobile robotic path planning,” IEEE Intelligent Computing and Integrated Systems, pp. 45-48, 2010.
[39] Ying Zhang , “Efficient Cache-Supported Path Planning on Roads” IEEE Transactions on Knowledge and Data Engineering , pp. 951-964, 2015.
[40] R. Fierro, F. L. Lewis. “Control of nonholonomic mobile robot using neural networks,” IEEE Transactions on Neural Networks, pp.589-600, 1998.
[41] S. M. Lavalle, J. J. Kuffner. “Randomized kinodynamic planning,” In Proceedings of 1999 IEEE International Conference on Robotics and Automation, Vol.1, pp.473,479, 1999.
[42] 嚴蔚敏,吳偉民,數據結構.北京:清華大學出版社,2008.
[43] Q. Wang, W. Wang, Y. Li. “A multi-RRT based hierarchical path planning method,” International Conference of IEEE Communication Technology, pp.971-975, 2012.
[44] Y. Xue, G. Tian and B. Huang, “Optimal Robot Path Besed on Danger Degree Map,” IEEE International Conference on Automation and Logistics, pp.1040,1045, 5-7, 2009.
[45] 陳依璟，「自走車之路徑規劃與位置追蹤」，國立中央大學，碩士論文，民國101年。
[46] 姜俊甫，「在室內環境中使用ALC-PSO演算法與危險度指標改良RRT路徑規劃」，國立中央大學，碩士論文，民國102年。
[47] 沈昱廷，「應用快速擴展隨機樹和人工魚群演算法及危險度於路徑規劃」國立中央大學，碩士論文，民國103年。

指導教授

鍾鴻源(Hung-Yuan Chung)

審核日期

2016-7-15

推文