ARIES-Mission2：零样本VLA框架实现大规模无人机任务快速生成
ARIES-Mission2: A Zero-Shot Vision-Language-Action Framework for Fast Large-Scale Aerial Mission Generation
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.
This report is categorized under AITOP's "Models" desk. Key entities and technical topics under continuous tracking include ARIES-Mission2, DeepSeek-V3, Molmo-7B, TSP.
Primary source verification: published directly by arXiv: DeepSeek with first-party authority.
