隨著科技進步各大企業積極推動工業自動化需求，串聯式機械手臂在許多產業應用扮演不可或缺的角色。機械手臂在工作環境運動過程中，無法完全避免障礙物的出現，故為了防止碰撞事件發生，目前採用制訂安全工作空間並搭配感測器為輔助判斷是否有障礙物影響機械手臂，如有異常則機械手臂停止動作，待專業人員排除問題後繼續運行。以上方法雖然確保機械手臂與工作人員安全，但卻降低了工作效率。
為了解決上述問題，本文希望建立機械手臂即時避障路徑的演算法系統，故選用機器人作業系統(Robot operating system, ROS)做為開發環境。本論文在ROS內建置機械手臂模型，以RGB-D攝影機作為感測器讀取深度資訊值，將障礙物外型及空間位置在模擬環境中以點雲資料型態顯示；障礙物模型與機械手臂模型進行碰撞偵測，若發生碰撞時透過開源運動規劃程式庫(Open motion planning library, OMPL)內的雙向快速擴展隨機樹(Rapidly-exploring random trees connect, RRT-Connect)演算法執行路徑規劃，完成機械手臂避障任務。
在ROS內已擁有在動作中偵測障礙物並且規劃避障路徑的功能，但其運算時間較久且產生避障路徑不穩定，經過深入研究後認為其避障路徑規劃的方法導致；為了克服上述問題，本論文基於RRT-Connect演算法改良提出一種動態避障路徑規劃的方法，在相同的實驗情境下達到減少運算時間及避障路徑行程的效果。在模擬環境與實體環境使用此改良方法與原始方法之實驗結果比較下，在模擬環境使用本文所提出之改良方法確實在運算時間與避障路徑上均優於原始方法。實體環境系統搭配機械手臂，需要將計算出之關節角度資料傳輸至控制器使其動作，因個人電腦運算能力不足令系統影像處理效率稍微下降，但改良方法的運算時間與避障路徑仍然勝過原始方法，從上述之實驗結果可以證明此改良方法之可行性。

With the advancement of science and technology, a large amounts of enterprises promote the industrial automation actively, and a series robotic arm is necessary for many different kinds of industry. There is always an unexpected incident in working environment, such as the appearance of obstacles. In order to prevent collision accidents, most of robotic arms are designed to work in the secure working space with sensors which are used to determine whether there is an obstacle or not. When an abnormality occurs, the robotic arm stops moving until the abnormality is solved by professionals. Although the above method ensures the safety of the robotic arms and the staffs, it also reduces the efficiency.
In order to solve the above problem, the thesis proposes an algorithm system for robotic arms with real-time obstacle avoidance in the Robot operating system (ROS). The thesis builds a robot arm model in the ROS, uses an RGB-D camera as a sensor to read the depth information data of obstacles, and then displays the shape and position of the obstacles in a simulated environment by point cloud data. Obstacle models and robotic arm models are used to compute collision detections. During the event of collision, a robotic arm uses the Rapidly-exploring random tree connect algorithm (RRT-Connect) in the OMPL to complete the obstacle avoidance task.
ROS is already equipped with the function of detecting obstacles and planning obstacle avoidance path, but its path planning process results in longer computing time and unstable obstacle avoidance paths. To overcome the above problems, this thesis proposes a dynamic obstacle-avoidance method based on the optimal RRT-Connect algorithm. This modified method make the path planning process to reduce computing time and obstacle-avoidance path lengths effectively. To compare the results of simulation and experiments by applying the original and modified methods, the modified method really provides the less computing time and the shorter obstacle-avoidance path lengths in both the simulations and experiments. Even though image processing takes more time in the experiments due to the specification limitations of the personal computer, the modified method is still better than the original method. Therefore, the demonstrated examples show that the proposed method in this study is feasible.

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