本研究主要探討無人機在室內進行搜索的演算法。現今無人機的應用越來越廣泛，然而，過去受限於無人機的導航方式多靠 GPS 訊號，使得無人機僅能在室外的空間使用，對於室內空間的自主飛行與應用則少見於文獻。李和蕭在 2016 年提出並驗證使用立體視覺進行無人機導航的可行性，使得無人機在室內自主飛行的可行性大增，因此本論文主要探討無人機如何在通道曲折，並有多個房間與叉路的室內，將飛行器飛至特定地點的路徑規畫方式。由於室內環境常常有多通道連通，本研究將之類比於迷宮，並把目前人工智慧對於機器鼠走迷宮的研究，應用到本主題。在本問題中，由於建築物可能有許多層，因此本研究把建築物的內部視為一立體迷宮，使用增強學習中的 Q-Learning 求解。此研究成果將大幅增加無人機的室內應用性。 This paper investigates the indoor path planning of an autonomous drone. Nowadays, drones have wider and wider applicability. However, due to the limitation of GPS navigation, drones are usually applied in an outdoor environment. Lee and Hsiao proposed an algorithm regarding indoor navigation using a stereo vision system in 2016, and this extends the application of drones to indoor environments. Hence, this paper investigates the path planning of a drone based on the stereo-vision navigation. The characteristics that there exist hallways, rooms, and folks in the road is similar to that of a maze. Therefore, algorithm of the path planning in a maze for a robot is employed in this paper. In our research, we apply the Q-Learning in the enforced learning to solve the indoor searching problem, and try to extend this algorithm to a 3D maze in the future. The result of our research will extend the applicability of drones to indoor domain in the future.
