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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/19005

Title: 適應性智慧型系統於自走車導引控制之應用
Application of Adaptive Intelligent Systems to Autonomous Vehicle Guidance and Control
Authors: 莊季高
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Keywords: 自走車;適應性控制;模糊系統;基因演算法;類神經網路;小腦模型控制 器;FPGA;自動導航;路徑規劃
Autonomous Vehicle;Adaptive Control;Fuzzy System;Genetic Algorithm;Neural Network;CMAC;GPS;FPGA;Automatic Guidance;Path Planning
Date: 2010-08
Issue Date: 2011-08-17T07:05:57Z
Abstract: 摘要:本研究是針對智慧型系統中,選擇適合於自走車導引控制之類神經網路、基因演 算法、模糊系統來設計控制器,最主要的目的是要尋找具備適應環境變化能力之適應 性控制法則,建立結構簡單、學習快速的適當演算方式,以便在硬體實現時,可使得 成本降至最低。本研究應用一簡便型自走車,Dr Robot X80,結合超音波距離感測模 組、紅外線測距感測器、人體運動感測模組、影像模組、雷射測距感測器、GPS 等, 進行自動導航、尋跡、避撞、追車、超車、巡邏、巡航等適應性控制之應用。本計畫 分三年完成,第一年(99/8-100/7)推導出 Adaptive Recurrent Neural Network (ARNN) 及 Adaptive Resource Allocating Network (ARAN) 學習法則,應用於追車跟隨及多車 隊形控制上,並利用基因演算法探討最佳化能量控制。在硬體整合方面,結合視覺與 距離感測元件,簡化影像處理技術,並應用嵌入式系統,實現於自走車導引控制上。 第二年(100/8-101/7)推導出 Adaptive CMAC 及 Adaptive GBF-CMAC 自走車控制法 則,並利用 Hopfield-Tank Neural Network 探討具室內動態避撞之最佳化路徑規劃方 式。另外亦結合本實驗室已研發之路邊停車及倒車入庫的自動控制功能,應用於自走 車室內清潔服務、室內公文傳遞等的控制上。在硬體整合方面,結合視覺與距離感測 元件與影像處理技術,應用嵌入式系統並結合紅外線室內定位感測系統,實現於自走 車室內導引控制上。第三年(101/8-102/7)結合模糊系統於類神經網路及CMAC,推導 出 Adaptive Type-1 與 Type-2 Fuzzy CMAC 及 Adaptive Linguistic Fuzzy Neural Network (ALFNN) 與 Adaptive Functional Fuzzy Neural Network (AFFNN) 學習法 則。另外亦結合第一年及第二年系統整合成果並加入GPS,應用於室外追車跟隨、超 車、多車隊形及動態避撞控制上。在硬體整合方面,則以DSP/FPGA 實現適應性智 慧型車輛自動導引控制器之設計與製作。
abstract:This research puts focus on applying intelligent systems, such as neural networks, genetic algorithms, and fuzzy systems, to autonomous vehicle guidance and control. The main purposes of this project are to develop adaptive learning rule for the vehicle control system that can adopt environment disturbance and to construct simple and fast learning algorithm that can lower hardware cost. This research utilizes a wheel mobile robot called the Dr Robot X80 with ultrasonic sensor, inferred sensor, motion sensor, camera, laser ranger, and GPS to perform automatic guidance, trajectory tracking, obstacle avoidance, cut in, patrol, and cruise control. This is a three-year project. At the first year, we will derive learning rules for adaptive recurrent neural network and adaptive resource allocating network and apply it to chasing control and formation control. Genetic algorithm is used to find optimal energy control. We will also integrate visual sensor, range sensor, image process, and embedded system to realize hardware control system. In the second year, we will derive adaptive CMAC and adaptive GBF-CMAC control rules for the autonomous vehicle. Hopfield-Tank neural network will be applied to dynamic obstacle avoidance and path planning. With parking control ability from previous work, the proposed autonomous vehicle will be able to perform floor cleaning and documentation delivering services. An inferred localization system will be used for indoor vehicle guidance and control. In the third year, we will derive proper learning rules of adaptive type-1 and type-2 fuzzy CMACs, adaptive linguistic fuzzy neural network, and adaptive functional fuzzy neural network for the vehicle control system. GPS will then be integrated in the control system for outdoor autonomous vehicle guidance and control tasks. Hardware controller will be implemented in a DSP/FPGA module. The feasibility of the proposed adaptive intelligent control system will be evaluated by the hardware test.
Relation: NSC99-2221-E019-039
URI: http://ntour.ntou.edu.tw/handle/987654321/19005
Appears in Collections:[通訊與導航工程學系] 研究計畫

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