本文爲希臘亞里士多德大學（作者：Michael Dille）的博士論文，共228頁。
在許多救援、監視和科學應用中，廣泛需要進行廣域偵察和地形測量，無人駕駛飛行器（UAV）正日益受到歡迎。本文考慮的任務是使用一個或多個無人機來定位感興趣的目標，提供連續的觀測視角，並在目標因任何原因丟失時快速重新捕獲跟蹤。
對於一般的無人機來說，由於傳感器視場小、無人機姿態估計不確定以及相對於環境規模的機動性有限，持續穩定跟蹤目標是一項困難的任務，需要不斷地處理觀測數據，重新計算飛行路徑或根據傳感器數據以最好地找到目標或保持其在視野範圍內。實現這一目標的現有策略只能提供對目標位置的糟糕估計，並且依賴於大量的啓發式或計算密集型軌跡生成來進行追蹤和搜索。
本文提出了觀測不確定性和環境結構開發的精細表示方法，特別是以典型的城市道路網爲例，來簡化和更好地建模問題。對於主動跟蹤的目標，通過爲高不確定性觀測設計的濾波器表示來證明極大地改進了位置估計，通過建模地形約束下的目標位置和運動空間縮減來提高跟蹤性能。對於沒有或只有大致已知先驗位置的目標，需要進行初始搜索，同時考慮經典貝葉斯概率搜索（對於隨機建模的運動目標）和新的道路網絡覆蓋策略（對於靜止或緩慢移動的目標）。最後，該算法被擴展到搜索和局部找回道路網絡中的逃逸規避目標，通過新的追蹤策略進行映射，這些策略在抽象或地面領域中得到了很好的研究，但尚未在空中應用看到解決方案。
在使用廣泛部署的飛行器進行的大量野外試驗中，已經驗證了本文設計的估計和跟蹤方法，並且在實際仿真中使用類似參數化的飛行器模型和控制接口對其他部件進行了評估，爲將演示算法直接應用於實踐奠定了基礎。
Across many rescue, surveillance, and scientific applications,there exists a broad need to perform wide-area reconnaissance and terrainsurveys, for which unmanned aerial vehicles (UAVs) are increasingly popular.This thesis considers the task of using one or more UAVs to locate an object ofinterest, provide continuous viewing, and rapidly re-acquire tracking should itbe lost for any reason.For both the common class of small field-launched UAVs considered aswell as larger UAVs, this is a difficult task due to a small available sensorfield of view, uncertain estimates of UAV pose, and limited maneuverabilityrelative to the scale of the environment, requiring constant processing ofobservations and recomputation of flight paths or sensor aiming to best findthe object or keep it in view. Existing strategies for accomplishing thisprovide poor estimates of the object’s location and rely on grossly heuristicor computationally intensive trajectory generation for both pursuit and search.
This thesis proposes careful representation of observationuncertainty and exploitation of environmental structure with particular focuson road networks typical of urban-like areas as means to simplify and better modelthe problem. For the case of actively tracked objects, greatly improvedlocation estimates are demonstrated through filter representations designed forhigh-uncertainty observations, as is increased pursuit performance by modelingterrain-constrained space reduction in object location and motion. Objectshaving no or only roughly known prior location require an initial search, forwhich both classical Bayesian probabilistic search (for stochastically-modeledmoving objects) and novel road network coverage strategies (for stationary orslow-moving objects) are considered. Finally, this is extended to search andlocal recapture of evasive adversaries in road networks through novel mappingsof pursuitevasion tactics that are well-studied in abstract or ground-baseddomains but have yet to see use in physical, particularly aerial, applications.Estimation and tracking aspects have been validated in extensivefield trials using widely-fielded air vehicles, and other components have been evaluatedin realistic simulation using similarly parameterized vehicle models andcontrol interfaces, laying the groundwork to directly apply the demonstratedalgorithms on real aircraft.
- 引言
- 問題研究
- 搜索與跟蹤的相關策略
- 地理定位與跟蹤
- 覆蓋與有效搜索
- 搜索與重新捕獲的保證
- 結論
附錄 小型無人機的魯棒自動視覺跟蹤
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