淡江大學機構典藏:Item 987654321/114771
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    题名: 基於AKF之感測器融合應用於移動型機器人之定位系統
    其它题名: AKF-based sensor fusion and application for localization system of mobile robot
    作者: 鄭期元;Cheng, Chi-Yuan
    贡献者: 淡江大學電機工程學系碩士班
    李世安
    关键词: Adaptive Kalman Filter;Localization System;Robot Operating System;sensor fusion;自適應卡曼濾波器;定位系統;感測器融合;機器人作業系統
    日期: 2017
    上传时间: 2018-08-03 15:03:32 (UTC+8)
    摘要: 本論文主要內容為提出並實現一個基於AKF之感測器融合應用於移動型機器人之定位系統。此系統可以使機器人在未知且平坦的室內環境進行準確之即時定位與建置地圖，以及融合兩種感測器之定位資訊，使定位更加強健。藉由此方法解決機器人在較複雜環境，使單一感測器之定位資訊出現誤差時，能夠有多感測器的定位資訊融合達到更加得定位效果。本論文使用自行設計並送廠製作出一個具有全向移動功能之移動型機器人。在硬體系統的部分，本文使用FPGA為馬達控制核心並自製成控制電路，微型電腦為主要程式策略之計算核心。使用雷射測距儀以及慣性感測器(Inertial Measurement Unit, IMU)，並在實際搭建模擬三房一廳之居家場地中進行研究並實現於真實環境中。本論文本文基於Hector SLAM(Simultaneous Localization and Mapping)演算法。利用雷射測距儀將當下的環境以動態的方式繪製晶格地圖與障礙物資訊，並與地圖資訊做掃描匹配估測於地圖中之位置。利用IMU及里程計資訊計算出相對位置之定位資訊，藉由兩者達成感測器融合之定位。並使用自適應蒙地卡羅定位(Adaptive Monte Carlo Localization, AMCL)演算法作為驗證方法。
    This paper proposes and implement an AKF-based Sensor Fusion and Application for Localization System of Mobile Robot. This system allows the robot to locate and build maps in an unknown and flat interior environment. As well as the integration of two sensor positioning information, So that positioning more robust. With this method to solve the robot in a more complex environment. So that a single sensor positioning information had error, can be more sensor positioning information fusion to achieve more positioning effect. This paper uses a self-designed and sent factory to produce a mobile robot with omni-directional wheel. In the hardware system, this article uses the FPGA as the motor control core and makes the control circuit. And Microcomputers are the core of the main program strategy. The use of LiDAR and IMU(Inertial Measurement Unit, IMU),in the actual set up to simulate the three-bedroom living room in the study and to achieve in the real environment. This paper is based on Hector SLAM (Simultaneous Localization and Mapping) algorithm. Use the LiDAR to draw the lattice map and obstacle information in a dynamic way and make a scan match with the map information to estimate the position in the map. Using the IMU and mileage information to calculate the relative position of the positioning information, through the two to achieve the positioning of the sensor fusion. And use the Adaptive Monte Carlo Localization (AMCL) algorithm as a validation method
    显示于类别:[電機工程學系暨研究所] 學位論文

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