隨著無人機技術的成熟，其在物流配送、巡檢與智慧製造等多點任務應用中日益普及。因此，如何在有限能源條件下規劃高效率飛行路徑，以降低能源消耗並縮短任務完成時間，已成為無人機應用研究的重要議題。本研究針對單機多點（Single-UAV Multi-Point， S-MP）與多機多點（Multi-UAV Multi-Point， M-MP）任務情境，分別在有載重與無載重條件下，進行最佳化路徑規劃之能源消耗與任務時間比較分析。 研究中首先建立無人機飛行之數學模型，將飛行距離、飛行時間與載重因素納入能源消耗計算，並定義總能源消耗與總任務完成時間作為效能評估指標。在路徑規劃方面，透過最佳化演算法求解不同任務點排列順序與機隊配置下之近似最佳解。針 對單機多點情境，無人機需依序完成所有任務點；而在多機多點情境中，則透過任務分配使多架無人機協同完成配送任務，以降低單一無人機之負載與飛行距離。 模擬結果顯示，在無載重情況下，多機多點能有效縮短總任務完成時間，但能源消耗未必顯著低於單機多點；而在有載重情況下，隨著載重影響飛行能耗增加，多機多點透過分散載重與縮短單機飛行距離，可有效降低總能源消耗並提升整體任務效率。研究結果證實，距離因素為影響無人機能源消耗之關鍵參數，而多機協同策略在高載重或任務點數增加時具有明顯優勢。 本研究建立載重修正之能耗模型並結合 TSP 路徑最佳化方法，提出一套可量化比較單機與多機多點任務效能之分析架構。;With the advancement of unmanned aerial vehicle (UAV) technology， its applications in multi-point missions such as logistics delivery， infrastructure inspection， and intelligent manufacturing have become increasingly widespread. Consequently， how to plan efficient flight paths under limited energy conditions in order to reduce energy consumption and shorten mission completion time has become an important research topic in UAV applications. This study focuses on two mission scenarios: Single-UAV Multi-Point (S-MP) and Multi-UAV Multi-Point (M-MP). Under both loaded and unloaded conditions， a comparative analysis of energy consumption and mission completion time is conducted based on optimized path planning strategies. First， a mathematical model of UAV flight is established by incorporating flight distance， flight time， and payload factors into the energy consumption calculation. Total energy consumption and total mission completion time are defined as the primary performance evaluation metrics. For path planning， optimization algorithms are employed to obtain near-optimal solutions for different task-point sequences and fleet configurations. In the S-MP scenario， a single UAV sequentially visits all task points. In contrast， in the M-MP scenario， multiple UAVs collaboratively complete the mission through task allocation， thereby reducing the workload and flight distance of each individual UAV. Simulation results indicate that under unloaded conditions， the M-MP approach can effectively shorten the total mission completion time; however， its energy consumption is not necessarily significantly lower than that of the S-MP approach. Under loaded conditions， as payload increases flight energy consumption， the M-MP strategy can reduce total energy consumption and improve overall mission efficiency by distributing payloads and shortening individual flight distances. The results demonstrate that flight distance is a key parameter affecting UAV energy consumption， and that multi-UAV collaborative strategies show clear advantages when payload increases or when the number of task points becomes larger. This study establishes a payload-corrected energy consumption model integrated with a Traveling Salesman Problem (TSP)-based path optimization method， and proposes a quantitative framework for comparing the performance of single-UAV and multi-UAV multi-point missions.
