这个框架让无人机看卫星图就能自动规划路线，比人工规划快3.6倍，飞行距离还缩短近10%，适合大规模任务。
ARIES-Mission2是一个零样本视觉-语言-动作（VLA）框架，将视觉语义感知与物理路线优化解耦。它用DeepSeek-V3解析自然语言任务，并用Molmo-7B进行零样本目标定位，再通过地理插值将像素坐标转为GPS航点。在UAV-VLPA-nano-30基准上，该框架的飞行距离为62.43公里，比未优化的VLA基线（79.66公里）缩短21.6%，比人工规划（69.00公里）缩短9.5%。30个任务总耗时575.40秒，平均每任务19.18秒，约为人类专家规划速度的3.6倍。其中TSP求解器每任务仅需0.16秒，而VLM推理占据主要耗时。
ARIES-Mission2: A Zero-Shot Vision-Language-Action Framework for Fast Large-Scale Aerial Mission Generation
Multimodal Large Language Models (MLLMs) have shown strong semantic understanding capabilities, but their direct use in low-altitude Unmanned Aerial Vehicle (UAV) mission generation remains limited by weak spatial optimization and inefficient route planning. To address this issue, we propose ARIES-Mission2, a zero-shot Vision-Language-Action (VLA) framework that decouples visual-semantic perception from physical route optimization. Given natural-language instructions and satellite imagery, ARIES-Mission2 first uses DeepSeek-V3 for task parsing and Molmo-7B for zero-shot target grounding, and then converts detected pixel locations into GPS waypoints through geospatial interpolation. To reduce the redundant backtracking caused by raw VLM-generated visiting orders, the back end formulates multi-target UAV traversal as a Traveling Salesperson Problem (TSP) and compares four candidate routes, including the raw VLM order and the routes optimized by PSO, GPSO, and IPSO. The minimum-cost closed-loop route is then selected for mission generation. Experiments on the UAV-VLPA-nano-30 benchmark show that ARIES-Mission2 achieves a total flight distance of 62.43 km, reducing the route length by 21.6% compared with the unoptimized VLA baseline (79.66 km) and by 9.5% compared with manual human planning (69.00 km). The complete 30-task workflow takes 575.40 s, averaging 19.18 s per task, which is approximately 3.6 times faster than human expert planning. Component-level timing shows that VLM inference dominates the runtime with 19.02 s per task, while the TSP solver requires only 0.16 s per task. Scalability analysis further indicates that the TSP module maintains lower growth in computation time as the number of targets increases.