空中自主載具（Autonomous Aerial Vehicle， AAV）逐漸應用於災害救援、環境監測、資料蒐集與臨時通訊等任務。相較於傳統地面節點，AAV 的模擬除須描述節點通訊與任務行為外，亦需考量三維移動、環境風場、飛行能源及任務期間的能源補給。然而，The ONE 模擬平台原有架構主要以二維地面節點為對象，對空中節點的三維移動、環境資訊與能源行為支援有限。因此，本研究以 The ONE 為基礎，設計並實作三維空間與無線充電擴充模組。系統將原有二維座標延伸至三維空間，並建立可調整解析度的網格空間模型，作為節點位置、環境風場與路徑規劃之間的共同空間索引。環境模組提供定向定速風場、CSV 外部風場匯入及 Von Kármán 空間風場三種模式，並透過統一的網格查詢介面取得 AAV 所在位置的局部風速。系統亦支援依預設時間間隔管理多個三維風場切片，並可透過時間索引與相鄰切片內插取得隨模擬時間變化的局部風速。飛行能源部分整合旋翼式無人機推進功率模型，以 AAV 的水平地面速度與環境風場的水平分量計算水平相對空速，並於節點實際發生高度變化時依垂直位移估算垂直飛行功率。系統另建立 Directional、Greedy 與 Energy-Aware 三種路徑規劃策略，分別以目的地方向、局部單步能源成本及完整路徑的累積能源成本進行路徑決策。為呈現任務期間的能源補給流程，本研究進一步以 TaskAAV 與 ChargeAAV 建立協作式近場無線充電應用情境，涵蓋充電需求產生、節點派遣、空中會合、位置調整、充電條件判定及能源轉移等程序。 本研究成果可分為模擬平台建構與平台功能驗證及應用兩個層次。在平台建構方面，完成三維座標、共享網格、環境風場查詢、旋翼式飛行能源、路徑規劃及協作式充電等功能於 The ONE 中的資料結構與更新流程整合。在功能驗證與應用方面，三維移動實驗確認 AAV 可於不同高度層執行不受道路拓樸限制的移動，並持續記錄完整三維軌跡；風場與能源實驗顯示，在相同飛行軌跡與地面速度下，不同風場條件會產生不同的能源消耗結果，說明局部風速、水平相對空速與推進功率能共同作用於節點能源更新。路徑規劃實驗使用 10 組不同亂數種子產生之 Von Kármán 風場進行重複比較，三種策略於所有測試中皆完成指定巡訪任務；其中 Energy-Aware 策略的平均能源消耗為 216.136 kJ，低於 Directional 的 219.106 kJ 與 Greedy 的 221.076 kJ，且具有較高的平均任務結束剩餘能源。協作式充電實驗則呈現 TaskAAV 在任務期間產生充電需求、與 ChargeAAV 完成會合與位置調整後進行能源補給的完整流程。此外，既有延遲限制 UAV 資料蒐集演算法的復現結果能呈現所需 UAV 數量隨 IoT 裝置數量、延遲上限及傳輸範圍變化的主要趨勢，說明擴充後的平台可進一步整合外部演算法並進行比較實驗。整體而言，本研究並非重新提出風場、旋翼推進功率或近場無線充電的理論模型，而是完成相關功能在 The ONE 中的系統化整合，使其在保留既有網路模擬能力的同時，可支援 AAV 三維移動、環境條件、飛行能源、路徑規劃與能源補給等節點與任務層級研究。;Autonomous Aerial Vehicles (AAVs) are increasingly used for disaster response， environmental monitoring， data collection， and temporary communication services. Compared with conventional ground nodes， AAV simulation requires not only the representation of communication and mission behavior but also three-dimensional mobility， environmental wind conditions， flight-energy consumption， and in-mission energy replenishment. However， the original architecture of The ONE simulator was developed primarily for two-dimensional ground-based nodes and provides limited support for the spatial， environmental， and energy-related characteristics of aerial platforms. This thesis therefore extends The ONE with three-dimensional spatial modeling and wireless charging capabilities. The original two-dimensional coordinate representation is extended to three-dimensional space， and an adjustable cell-based spatial model is introduced as a shared spatial index for node positions， environmental wind data， and path planning. The environmental module supports three wind-field configurations: uniform wind， CSV-based external wind-field import， and spatial wind fields generated using a Von Kármán spectrum. A common grid-query interface allows AAVs to obtain local wind information at their current positions. The platform also supports multiple three-dimensional wind-field slices at predefined time intervals and can obtain time-varying local wind information through temporal indexing and interpolation between adjacent slices. Flight-energy consumption is estimated using rotarywing propulsion-power models. For horizontal flight， the horizontal relative airspeed is calculated from the AAV horizontal ground velocity and the horizontal components of the local wind field， while vertical flight power is estimated separately from the actual change in altitude. Three path-planning strategies are implemented: Directional， Greedy， and Energy-Aware planning， which make decisions based on destination direction， local one-step energy cost， and cumulative path energy cost， respectively. To represent in-mission energy replenishment， a cooperative near-field wireless charging scenario consisting of TaskAAVs and ChargeAAVs is further implemented， including charging-request generation， node dispatch， aerial rendezvous， positional adjustment， charging-condition evaluation， and energy transfer. The contributions of this work are organized into two levels: simulation-platform construction and platform validation and application. At the platform level， three-dimensional coordinates， a shared spatial grid， environmental wind-field queries， rotary-wing flight-energy estimation， path planning， and cooperative charging are integrated into the data structures and update procedures of The ONE. At the validation and application level， the three-dimensional mobility experiment confirms that AAVs can perform road-independent movement across different altitude layers while maintaining complete three-dimensional trajectory records. The wind-and-energy experiment shows that different wind conditions produce different energy-consumption profiles under identical routes and ground-speed settings， demonstrating the integration of local wind information， horizontal relative airspeed， propulsion power， and node energy updates. The path-planning experiment is repeated using ten Von Kármán wind-field realizations generated with different random seeds. All three strategies complete the prescribed patrol mission in every run. The Energy-Aware strategy achieves a mean energy consumption of 216.136 kJ， compared with 219.106 kJ for Directional and 221.076 kJ for Greedy， while also retaining higher mean residual energy at mission completion. The cooperative charging experiment demonstrates the complete system-level workflow in which a TaskAAV generates a charging request， meets a ChargeAAV at a rendezvous point， performs positional adjustment， and receives energy replenishment during mission execution. In addition， the replication of an existing delay-constrained UAV data-collection algorithm reproduces the main trends in the required number of UAVs as the number of IoT devices， delay bound， and transmission range vary， illustrating the ability of the extended platform to incorporate external algorithms and support comparative experiments. Rather than proposing new theoretical formulations for wind fields， rotary-wing propulsion power， or near-field wireless power transfer， this work focuses on their systematic integration into The ONE. The resulting platform preserves the simulator’s existing networking capabilities while extending it to support node- and mission-level studies of three-dimensional AAVmobility， environmental conditions， flight energy， path planning， and energy replenishment.
