2026-03-12
【學術亮點】透過一種新型的LSTM-GRU混合深度學習架構增強路面溫度預測，並提高其可解釋性
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Intelligent Service: Large-scale Agricultural AI Models
【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧服務：可大規模擴展之農業AI 模型【土木工程學系楊明德終身特聘教授】
上架日期2026-01-13
【Department of Civil Engineering / Ming-Der Yang / Tenured Distinguished Professor】
智慧服務：可大規模擴展之農業AI 模型【土木工程學系楊明德終身特聘教授】
| 論文篇名 | 英文：Real-Time UAV Flight Path Prediction Using GRU Networks for Autonomous Site Assessment 中文：基於GRU網路的無人機即時飛行路徑預測及其在自主場地評估中的應用 |
| 期刊名稱 | Drones |
| 發表年份, 卷數, 起迄頁數 | 2026,10, no.56 |
| 作者 | Yared Bitew Kebede, Ming-Der Yang(楊明德)*, Henok Desalegn Shikur, Hsin-Hung Tseng |
| DOI | 10.3390/drones10010056 |
| 中文摘要 | 無人機（UAV）憑藉其快速採集高解析度空間資料的能力，已成為基礎設施巡檢、公共安全監控、交通監控、環境感知和目標追蹤等關鍵領域不可或缺的工具。然而，保持穩定且精確的飛行軌跡仍然是一項重大挑戰，尤其是在動態或不確定環境下執行自主任務時。本研究提出了一個基於門控循環單元（GRU）的新型飛行路徑預測框架，該框架利用歷史感測器飛行數據，可對無人機的四維坐標（東向坐標 (X)、北向坐標 (Y)、高度 (Z) 和時間 (T)）進行單步和多步預測。模型性能已透過與傳統循環神經網路架構的對比進行了系統驗證。在未見過的測試數據上，GRU 模型在單步預測中展現出更高的預測精度，平均絕對誤差 (MAE) 為 0.0036，均方根誤差 (RMSE) 為 0.0054，相關係數 (R² )為 0.9923。至關重要的是，在旨在模擬GPS中斷等實際挑戰的多步驟預測中，GRU模型保持了卓越的穩定性和低誤差，證實了其對誤差累積的穩健性。研究結果表明，基於GRU的模型是無人機軌跡預測的高精度、高運算效率和高可靠性解決方案。該框架增強了自主導航能力，並直接支援高保真攝影測量所需的資料完整性，從而確保在複雜動態環境中進行可靠的場地評估。 |
| 英文摘要 | Unmanned Aerial Vehicles (UAVs) have become essential tools across critical domains, including infrastructure inspection, public safety monitoring, traffic surveillance, environmental sensing, and target tracking, owing to their ability to collect high-resolution spatial data rapidly. However, maintaining stable and accurate flight trajectories remains a significant challenge, particularly during autonomous missions in dynamic or uncertain environments. This study presents a novel flight path prediction framework based on Gated Recurrent Units (GRUs), designed for both single-step and multi-step-ahead forecasting of four-dimensional UAV coordinates, Easting (X), Northing (Y), Altitude (Z), and Time (T), using historical sensor flight data. Model performance was systematically validated against traditional Recurrent Neural Network architectures. On unseen test data, the GRU model demonstrated enhanced predictive accuracy in single-step prediction, achieving a MAE of 0.0036, Root Mean Square Error (RMSE) of 0.0054, and a (R2) of 0.9923. Crucially, in multi-step-ahead forecasting designed to simulate real-world challenges such as GPS outages, the GRU model maintained exceptional stability and low error, confirming its resilience to error accumulation. The findings establish that the GRU-based model is a highly accurate, computationally efficient, and reliable solution for UAV trajectory forecasting. This framework enhances autonomous navigation and directly supports the data integrity required for high-fidelity photogrammetric mapping, ensuring reliable site assessment in complex and dynamic environments. |
| 發表成果與AI計畫研究主題相關性 | GRU 預測的無人機軌跡模型在多步驟預測過程中對累積誤差傳播的抵抗能力為自主任務提供了關鍵的安全緩衝，使無人機即使在間歇性感測器故障或 GPS 訊號中斷的情況下也能保持穩定的導航和任務連續性。 |
