隨著無人飛行機的普及，機器人可幫助人類解決實際且高維度問題，例如搜尋感染農作物、搜尋建築結構裂痕、山難搜救、搜尋盜獵濫伐等等。然而此類問題需在3D空間中自主飛行，執行任務時需要即時處理龐大的運算量。這些問題已被證明為NP-hard，所以目前無人飛行機仍需專業操控員。本研究將蒐集操控員在複雜3D空間中執行任務時的資料，再將資料提供給無人飛行機學習，使無人飛行機可比擬甚至超越專業操控員的能力。因此本計畫將分為三年執行，第一年針對單一無人機的研究，第二年針對多無人機(無人機群)的研究。此計畫之研究目標將針對無人機(群)在3D環境下的搜尋問題之三項子議題探討: (1) 無人機(群)的搜尋能力可以超越操控員嗎? (2) 無人機(群)的搜尋問題可學習嗎? 若可，需要多少資料量?(3) 無人機(群)的搜尋效能為最佳解的幾成效能?本計畫目標除了研發技術外，亦在人工智慧技術上佈局多國專利。 ;Since the popularity of unmanned aerial vehicles (UAVs), robots can help humans solving practical and high-dimensional problems (e.g., detection of infected plants, search for structural failure, mountain rescue and search for illegal logging). These problems involve autonomously guidance in 3D environments and need to real-time process large data. These problems are proved as NP-hard problems. Hence, professional pilots are still necessary for UAVs. This project is to collect the UAV’s flying data via pilots’ control in complex 3D environments. And then, the UAV will learn from the data. It could make the flying ability of UAV be equal to or greater than the flying ability of pilots. The project will take three years. The focus of the first year is a UAV search and the focus of the second year is UAVs search. The goal of this project is to explore three key issues of 3D search problems. (1) The ability of UAV(s) is better than human pilots? (2) The UAV(s) search problems are learnable? If yes, how much data the UAV(s) need? (3) What’s the optimality of the proposed solutions? The goal of this project includes research and applying patent of artificial intelligence technologies.
