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標題: 無人飛行系統基於機器學習於避障與目標追隨之研究
Implementation of Obstacle Avoidance and Target Following Based on Machine Learning for Unmanned Aerial Vehicle
作者: 林祐弘
Yu-Hung Lin
關鍵字: 無人機
Unmanned Aerial Vehicle
Machine Learning
Neural Network
Target Following
Obstacle Avoidance
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摘要: 無人載具近年來蓬勃發展,尤其是旋翼型無人飛行載具,許多空拍影像出現在各大媒體及網站,是現代科技討論熱烈的產業,世界各地爭相開發無人載具以提高作業效率。而在農業的應用中有自動噴灑農藥以及在空中航拍評估稻田所需的施肥量之應用,本論文整合完成一具備姿態感測、平衡控制、機器視覺辨識追隨以及障礙物形狀分類與迴避等能力,適用於低空果園及樹林木間穿梭之無人飛行載具。期望以機器學習為主要的控制模式,整備追蹤目標物與自主避障的功能。本研究使用大疆公司所生產的F450機架為主體,姿態計算採陀螺儀與加速度計取得的資料以互補濾波器進行姿態感測的數據融合演算,再將此數據以雙環式PID控制器進行馬達輸入的計算,以達到旋翼平衡;目標物辨識採機器視覺的方式進行識別,而雷射測距儀則用於取得目標物的距離以及對障礙物進行形狀分類;在運動控制上,載具會依據不同形狀的障礙物而有不同的避障策略。最終實驗結果顯示互補濾波器能有效改善陀螺儀的穩態誤差以及加速度計的高頻震盪;而在平衡控制上,雙環式PID控制上穩定性明顯優於單環式PID控制,然而在載具追隨與避障的實驗中,因顧及旋翼控制板的穩定性以及操作人員的安全,以地面式三輪全向無人載具進行測試。最終驗證結果顯示,本研究基於機器學習-倒傳遞類神經網路法所建構之載具系統已初步達成有效追隨目標、正確分類障礙物形狀以及賦予避障能力的研究目標。
Unmanned aerial vehicle (UAV) is flourish recently,; especially the quadcopter unmanned aerial vehicles. There are many aerial photography shown on the news and the websites and most of these photos are the contributions of the UVA. Since UAV is a hot topic of modern industry, manufacturers all over the world are devoted to developing the technology and application of UAV. In agriculture, many applications are conducted by UAV including self-spraying pesticide and evaluating the amount of fertilizer needed in field. This thesis develops an integrated system that consist of orientation sensing, balance control, machine vision recognition and shape sorting and avoidance of obstacle with which the UVA will meet the requirement of a low altitude shuttle in orchards and forest. This thesis also focus on employing machine learning as the main control core to achieve the performances of target following and obstacle avoidance for UAV. In order to maintain the balance of a quadcopter, the orientation data from gyroscope and accelerometer will be the inputs of a double loop PID controller. Target is recognized by machine vision. Distances and shapes of obstacles are defined by a laser rangefinder that mounted on the vehicle. The control of UAV, depending on the shape of the obstacle, execute different obstacle avoidance strategies. The results of experiment indicated that the complementary filter is better than gyroscope and accelerometer since it fixes steady-state error of gyroscope and oscillation in high frequency of accelerometer. Double-loop PID controller is more stable than single-loop PID controller is. Due to stability of control board and safety of operators in target following and obstacle avoidance experiment, a three wheels omni robot was employed to conduct the performance verification. The final results validate the novel UAV with capabilities of target following, obstacle sorting and obstacle avoidance equipped by the machine learning -the back propagation neural network.
文章公開時間: 2018-07-25
Appears in Collections:生物產業機電工程學系



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