臺灣博碩士論文加值系統

在本論文中，我們提出一種基於模糊邏輯與深度影像的人工位能方法，以實現多架四軸飛行器在三維空間中的避障和防撞策略。利用深度影像判別障礙物分布，並透過演算法計算其中心位置以獲得避障方向。除此之外，為了使無人機速度不被避障速度向量影響飛行速度，透過模糊控制方法將避障方向與當前速度向量結合，使無人機得以在不改變速度前提下實現避障。為了解決在多四軸飛行器之編隊飛行中，每架四軸飛行器在進行避障時會有互相碰撞的風險，因此，本論文提出一種改良式的人工位能法，加強垂直移動方向的防碰撞排斥力以避免四軸飛行器彼此之間發生碰撞及減少視覺感測器被遮蔽的風險，此外所產生的避障排斥力也可達到避障的效果。經由區域網路連結多台搭載Pixhawk、飛行控制板和Raspberry Pi 4 Model B的四軸飛行器與電腦進行實際飛行以驗證所提之結果。經由實驗結果可驗證所開發之四軸飛行器及設計方法之可行性及有效性。

In this paper, we proposed an artificial potential field based on fuzzy logic with depth image to achieve the obstacle avoidance and collision avoidance for multiple quadcopters in 3D space. The depth image is used to determine the distribution of obstacles, and figure out the position of center by the algorithm to obtain the direction of obstacle avoidance. In addition, in order to keep the quadrotor speed, which may be affected during the execution of the algorithm, the proposed fuzzy-based control scheme that combined the direction of obstacle avoidance with the current speed vector to achieve the obstacles avoidance and maintain the speed of the quadcopters. In the formation flight of multiple quadcopters, there is a risk of collision when each quadcopter performs obstacle avoidance. In order to deal with this problem, an improved artificial potential field is proposed and the vertical anti-collision repulsive force is thus enhanced for avoiding the collisions between each of quadcopters and reducing the risk of obscuring visual sensors, and further the produced avoidance repulsion force can achieve the obstacle avoidance. The proposed results are demonstrated by the local area network which connected with the multiple quadcopters that installed the Pixhawk flight control board and Raspberry Pi 4 Model B and computer. The experimental results show the effectiveness and feasibility of the implemented quadrotors and the proposed design method.

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章緒論 1
1.1 前言 1
1.2 研究方法與目的 2
1.3 論文貢獻與文獻探討 3
1.4 論文架構 5
第二章四軸飛行器飛行原理與動力模型推導 6
2.1 四軸飛行器的飛行原理 6
2.2 四軸飛行器動力模型系統 7
第三章基於深度影像之避障演算法 11
3.1 Intel RelaSense D400 系列深度相機 11
3.2 模糊控制系統 12
3.3 人工位能法 15
3.4 避障之排斥力 15
3.5 基於模糊邏輯之避障合力 18
第四章具有防碰撞功能之編隊飛行系統 23
4.1 虛擬結構法 24
4.2 機體間之排斥力 25
4.3 基於模糊邏輯之總合力 26
4.4 模糊邏輯結合深度影像之人工位能法控制流程 29
第五章實驗設備介紹與結果分析 32
5.1 四軸飛行器架構 32
5.2 PX4 Autopilot 38
5.3 機器人作業系統(Robot Operating System, ROS) 40
5.4 Gazebo 42
5.5 模擬結果 44
5.6 實驗環境與通訊架構 62
5.6.1 實驗環境 62
5.6.2 軟硬體設置 63
5.6.3 通訊架構 64
5.6.4 訊息交換架構 65
5.6.5 四軸飛行器的深度視野 67
5.7 實際飛行結果 69
第六章結論及未來展望 85
6.1 結論 85
6.2 未來展望 85
參考文獻 87

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