進階搜尋


下載電子全文  
系統識別號 U0026-2201202007520300
論文名稱(中文) 利用改良式差分進化之區間二型遞迴模糊小腦模型於行動機器人協同搬運控制
論文名稱(英文) Using Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller Based on Improved Differential Evolution for Cooperative Carrying Controller of Mobile Robots
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 108
學期 1
出版年 109
研究生(中文) 林子超
研究生(英文) Tzu-Chao Lin
學號 P98011048
學位類別 博士
語文別 英文
論文頁數 81頁
口試委員 指導教授-陳朝鈞
共同指導教授-林正堅
口試委員-謝孫源
口試委員-連震杰
召集委員-潘欣泰
口試委員-陳文中
口試委員-陳青文
口試委員-蘇國和
中文關鍵字 行動機器人  模糊控制  導航控制  合作搬運  區間二型遞迴式模糊  小腦模型控制器  差分進化演算法 
英文關鍵字 Navigation control  Cooperative carrying  Interval type-2 recurrent fuzzy cerebellar model articulation controller  Dynamic grouping differential evolution  Wall-following carrying  Toward goal carrying 
學科別分類
中文摘要 近年來行動機器人已廣泛應用於各種領域中,主要技術包含導航、避障及物體搬運等,如何有效的應用這些技術並使機器人完成指定的任務就成為值得研究探討的議題,特別是應用導航技術於未知環境中仍存在著許多研究議題,因此本文提出一種在未知環境中應用行動機器人的合作搬運方法來到達到此一目的。
在搬運的過程中,行動機器人需要避開障礙物並不讓搬運的物體發生掉落或碰撞到障礙物,本文中採用一個狀態管理者(SM),依據環境的改變切換成沿牆搬運(WFC)及朝往目標搬運(TGC)等二種模式,並提出具動態分群之差分進化演算法(DGDE)之區間二型遞迴式模糊小腦模型控制器(IT2RFCMAC)執行沿牆搬運控制(WFC),利用這種增強式學習讓機器人能在未知環境中自我學習沿牆控制,無需預先收集訓練資料,即可將使用訓練完成的沿牆控制器來實現機器人合作搬運沿牆控制。
實驗結果顯示所提出的動態分群之差分進化演算法在沿牆搬運控制的效能優於其它演算法。並且在合作搬運導航之實驗結果也顯示出所提出的方法可成功的將搬運物體順利地到達目的地。
英文摘要 In recent years, mobile robot has been widely utilized in various fields such as transportation navigation, obstacle avoidance and object carrying. Implementing the above techniques and enabling the robots to complete the assigned task are hard challenge, especially in an unexploited environment. Therefore, this study proposes an effective, cooperative carrying method for mobile robots in unknown environments.
Our research goal is that the mobile robot should keep away from obstacles to avoid collision and prevent the carrying objects from dropping down during the transportation process. In this study we propose a state manager (SM) designed to assist the mobile robots so that they can switch operation between wall-following carrying (WFC) and toward goal carrying (TGC) by different external condition. Also we propose a controlling model, interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC), embedded with a modified evolutionary optimization, dynamic grouping differential evolution (DGDE), to implement the WFC and TGC. By adopting reinforcement learning strategy, mobile robots equip with adaptively wall-following control and that makes cooperative carrying control in real.
Experiment results show that proposed dynamic grouping differential evolution algorithm performs superior to other algorithms in wall-following carrying, and mobile robots can complete cooperative carrying successfully to reach the goal.
論文目次 1. INTRODUCTION 1
1.1 MOTIVATION 1
1.2 LITERATURE REVIEW 2
1.3 ORGANIZATION OF DISSERTATION 5
2. BACKGROUND AND RELATED WORKS 6
2.1 MOBILE ROBOT 6
2.2 OVERVIEW OF NAVIGATION CONTROL OF MOBILE ROBOT 7
2.2.1 PRADHAN’S METHOD 7
2.2.2 FAROOQ’S METHOD 9
2.2.3 JANGLOVA’S METHOD 9
2.2.4 METHOD OF ZHU & YANG 10
2.2.5 METHOD OF KNUDSON & TUMER 12
2.2.6 METHOD OF KIM & CHWA 13
3. ARTIFICIAL NEURAL NETWORK AND PARAMETERS LEARNING 15
3.1 NEURAL NETWORK 15
3.1.1 MULTILAYER NEURAL NETWORK 15
3.1.2 FUZZY NEURAL NETWORK 16
3.1.3 CEREBELLAR MODEL ARTICULATION CONTROLLER 19
3.2 PARAMETER LEARNING ALGORITHM 21
3.2.1 BACK PROPAGATION ALGORITHM (BP) 21
3.2.2 DIFFERENTIAL EVOLUTION ALGORITHM (DE) 22
3.2.3 ADAPTIVE DIFFERENTIAL EVOLUTION (JADE) 24
3.2.4 RANKING-BASED DIFFERENTIAL EVOLUTION (RANK-DE) 25
3.2.5 PARTICLE SWARM OPTIMIZATION (PSO) 26
3.2.6 ARTIFICIAL BEE COLONY ALGORITHM (ABC) 27
4. INTERVAL TYPE-2 RECURRENT FUZZY CEREBELLAR MODEL ARTICULATION CONTROLLER (IT2RFCMAC) 30
4.1 MODEL INTRODUCTION 30
4.1.1 STRUCTURE OF IT2RFCMAC 30
4.1.2 DYNAMIC GROUPING DIFFERENTIAL EVOLUTION (DGDE) ALGORITHM 34
4.2 PERFORMANCE EVALUATION 39
4.2.1 DYNAMIC SYSTEM IDENTIFICATION 39
4.2.2 MACKEY–GLASS CHAOTIC TIME SERIES PREDICTION 45
4.2.3 NOISE TOLERANCE 51
5. NAVIAGTION OF COOPERATIVE CARRYING 54
5.1 WALL-FOLLOWING CONTROL OF MOBILE ROBOT 54
5.1.1 CONTROL SCHEME 54
5.1.2 EXPERIMENTAL RESULTS 57
5.2 WALL-FOLLOWING CONTROL OF COOPERATIVE CARRYING 61
5.2.1 CONTROL SCHEME 62
5.2.2 EXPERIMENTAL RESULTS 64
5.3 NAVIGATION CONTROL OF COOPERATIVE CARRYING 68
5.3.1 NAVIGATION CONTROL SCHEME 68
5.3.2 EXPERIMENTAL RESULTS 72
6. CONCLUSIONS AND FUTURE WORKS 74
6.1 CONCLUSIONS 74
6.2 FUTURE WORKS 74
7. REFERENCES 76
參考文獻 [1] Mikko Lauri, Risto Ritala, “Planning for robotic exploration based on forward simulation,” Robotics and Autonomous Systems, Volume 83, Pages 15-31, Sep. 2016.
[2] Golak Bihari Mahantaa, Amruta Routa, Gunji Bala Muraliaa, BBVL Deepaka, BB Biswal, “Application of hybrid Nelder-Mead Bat algorithm to improve the grasp quality during the automated robotic grasping,” Procedia Computer Science, Volume 133, Pages 612-619, 2018.
[3] Lars Andre Langøyli Giske, Emil Bjørlykhaug, Trond Løvdal, Ola Jon Mork, “Experimental study of effectiveness of robotic cleaning for fish-processing plants,” Food Control, Volume 100, Pages 269-277, June 2019.
[4] Galibjon M.Sharipov, Dimitris S. Paraforos, Hans W. Griepentrog, “Implementation of a magnetorheological damper on a no-till seeding assembly for optimising seeding depth,” Computers and Electronics in Agriculture, Volume 150, Pages 465-475, July 2018.
[5] H. A. M. Williams, M. H. Jones, M. Nejati, M. J. Seabright, J. Bell, N. D. Penhall, J. J. Barnett, M. D.Duke, A. J. Scarfe, H. S. Ahna, J. Y. Lim, B. A. MacDonald, “Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms,” Biosystems Engineering, Volume 181, Pages 140-156, May 2019.
[6] Erasmo Gabriel Martinez-Soltero, Jesus Hernandez-Barragan, “Robot Navigation Based on Differential Evolution,” IFAC-PapersOnLine, Volume 51, Issue 13, Pages 350-354, 2018.
[7] Liu Meichen, Chen Jun, Zhao Xiang, Wang Lu, Tian Yongpeng, “Dynamic obstacle detection based on multi-sensor information fusion,” IFAC-PapersOnLine, Volume 51, Issue 17, Pages 861-865, 2018.
[8] Hua Deng, Guiyang Xin, Guoliang Zhong, Michael Mistry, “Object carrying of hexapod robots with integrated mechanism of leg and arm,” Robotics and Computer-Integrated Manufacturing, Volume 54, Pages 145-155, Dec. 2018.
[9] H. Maaref, C. Barret, “Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment,” Control Engineering Practice, Volume 8, Issue 7, Pages 757-768, July 2000.
[10] Jiahai Liang, “A Path Planning Algorithm of Mobile Robot in Known 3D Environment,” Procedia Engineering, Volume 15, Pages 157-162, 2011.
[11] Chaoxia Shi, Yanqing Wang, Jingyu Yang, “A local obstacle avoidance method for mobile robots in partially known environment,” Robotics and Autonomous Systems, Volume 58, Issue 5, Pages 425-434, 31 May 2010.
[12] Madjid Hank, Moussa Haddad, “A hybrid approach for autonomous navigation of mobile robots in partially-known environments,” Robotics and Autonomous Systems, Volume 86, Pages 113-127, December 2016.
[13] Saroj Kumar Pradhan, Dayal Ramakrushna Parhi, Anup Kumar Panda, “Fuzzy logic techniques for navigation of several mobile robots,” Applied Soft Computing, Volume 9, Issue 1, Pages 290-304, January 2009.
[14] Mohammed Faisal, Ramdane Hedjar, Mansour Al Sulaiman, and Khalid Al-Mutib, “Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment,” International Journal of Advanced Robotic Systems, Volume 10, Issue 37, 2013.
[15] Umar Farooq, K. M. Hasan, Ghulam Abbas, Muhammad Usman Asad, “Comparative analysis of zero order Sugeno and Mamdani fuzzy logic controllers for obstacle avoidance behavior in mobile robot navigation,” The 2011 International Conference and Workshop on Current Trends in Information Technology (CTIT 11), Pages 113–119.
[16] Danica Janglová, “Neural Networks in Mobile Robot Motion,” International Journal of Advanced Robotic Systems, Volume 1, Issue 1, March 2004.
[17] Anmin Zhu and Simon X. Yang, “Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments,” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), Volume: 37 , Issue: 4, Pages 610–621, July 2007.
[18] Matt Knudson and KaganTumer, “Adaptive navigation for autonomous robots,” Robotics and Autonomous Systems, Volume 59, Issue 6, Pages 410-420, June 2011.
[19] Cheol-Joong Kim and Dongkyoung Chwa, “Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network,” IEEE Transactions on Fuzzy Systems, Volume 23 , Issue 3 , June 2015.
[20] İlker Bekir Topçu, Mustafa Sarıdemir, “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic,” Computational Materials Science, Volume 41, Issue 3, Pages 305-311, January 2008.
[21] Mikael Manngård, Jan Kronqvist, Jari M. Böling, “Structural learning in artificial neural networks using sparse optimization,” Neurocomputing, Volume 272, Pages 660-667, 10 January 2018.
[22] J.-S.R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, Volume 23 , Issue 3 , May/Jun 1993.
[23] Wen Ziteng, Xie Linbo, Feng Hongwei, Tan Yong, “Infrared flame detection based on a self-organizing TS-type fuzzy neural network,” Neurocomputing, Volume 337, Pages 67-79, 14 April 2019.
[24] Yue Hou, Long Zhao, Huaiwei Lu, “Fuzzy neural network optimization and network traffic forecasting based on improved differential evolution,” Future Generation Computer Systems, Volume 81, Pages 425-432, April 2018.
[25] Tomohiro Takagi, Michio Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, Volume SMC-15 , Issue 1 , Jan.-Feb. 1985.
[26] J. S. Albus, “A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC),” Journal of Dynamic Systems, Measurement, and Control, Volume 97, Issue 3, Pages 220-227, September 1975.
[27] J. S. Albus, “Data Storage in the Cerebellar Model Articulation Controller (CMAC),” Journal of Dynamic Systems, Measurement, and Control, Volume 97, Issue 3, September 1975.
[28] David E. Rumelhart, Geoffrey E. Hinton & Ronald J. Williams, “Learning representations by back-propagating errors,” Nature, Volume 323, Pages 533–536, October 1986.
[29] Henry J. Kelley, “Gradient Theory of Optimal Flight Paths,” ARS Journal, Volume 30, Number 10, Pages 947–954, October 1960.
[30] J. T. Lalis, B. D. Gerardo, Y. Byun, “ An Adaptive Stopping Criterion for Backpropagation Learning in Feedforward Neural Network,” International Journal of Multimedia and Ubiquitous Engineering, Volume 9, Number 8, Pages 149–156, 2014.
[31] R. Storn, “On the usage of differential evolution for function optimization,” Proceedings of North American Fuzzy Information Processing, Pages 519–523, June 1996.
[32] R. Storn, K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, Volume 11, Number 4, Pages 341–359, 1997.
[33] Jingqiao Zhang, Arthur C. Sanderson, “JADE: Adaptive Differential Evolution With Optional External Archive,” IEEE Transactions on Evolutionary Computation, Volume 13 , Issue 5, Pages 945–958, Oct. 2009.
[34] Wenyin Gong, Zhihua Cai, ”Differential Evolution With Ranking-Based Mutation Operators,” IEEE Transactions on Cybernetics, Volume 43 , Issue 6, Pages 2066–2081, Dec. 2013.
[35] J. Kennedy, R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN’95 - International Conference on Neural Networks, Volume 4, Pages 1942–1948, Nov. 1995.
[36] Yang Guangyou, “A modified particle swarm optimizer algorithm,” 2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, Pages 2675–2679, Aug. 2007.
[37] Dervis Karaboga, “An idea based on honey bee swarm for numerical optimization,” TECHNICAL REPORT-TR06, Oct. 2005.
[38] Dervis Karaboga, Bahriye Basturk, ”A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, Volume 39, Pages 459–471, 2007.
[39] Oscar Castillo, Patricia Melin, “A review on the design and optimization of interval type-2 fuzzy controllers,” Applied Soft Computing, Volume 12, Issue 4, Pages 1267-1278, April 2012.
[40] P.S. Sastry, G. Santharam, K.P. Unnikrishnan, “Memory neuron networks for identification and control of dynamical systems,” IEEE Transactions on Neural Networks, Volume 5 , Issue 2 , Pages 306–319, March 1994.
[41] Chia-Feng Juang, “A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms,” IEEE Transactions on Fuzzy Systems, Volume 10 , Issue 2 , Pages 155–170, Apr 2002.
[42] J. Doyne Farmer, John J. Sidorowich, ”Predicting chaotic time series,” Physical Review Letters, Volume 59, Issue 8, Pages 845–848, Aug. 1987.
[43] K. -R. MüllerA. J. SmolaG. RätschB. SchölkopfJ. KohlmorgenV. Vapnik, “Predicting time series with support vector machines,” International Conference on Artificial Neural Networks, Volume 1327, Pages 999–1004, 1997.
[44] J.-S.R. Jang, C.-T. Sun, “Predicting chaotic time series with fuzzy if-then rules,” Second IEEE International Conference on Fuzzy Systems, Pages 1079–1084, March 1993.
[45] P. Raja and S. Pugazhenthi, "Path planning for a mobile robot in dynamic environments," International Journal of the Physical Sciences, Volume 6(20), Pages 4721-4731, 23 September, 2011.
[46] Chia-Feng Juang, Yu-Cheng Chang,"Evolutionary-group-based particle-swarm-optimized fuzzy controller with application to mobile-robot navigation in unknown environments," IEEE Transactions on Fuzzy Systems, Volume 19 , Issue 2 , April 2011.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2020-02-05起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-02-05起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw