ARGUS为战术无人车生成兼顾任务与风险的实时路径规划方案
ARGUS: A Framework for Risk-Aware Path Planning in Tactical UGV Operations
- 融合地形、威胁和指挥官意图，动态生成优化路径
- 支持实时调整，应对战场突发情况，提升安全性
- 已通过葡萄牙陆军实战验证，可对接现有控制系统
本论文提出ARGUS框架，用于战术环境下无人地面车辆（UGV）的任务规划。系统接收地理空间地形数据、敌方威胁及其可能位置的军事情报，以及指挥官设定的任务优先级，通过集成模块处理信息，生成在任务目标与威胁及地形风险间平衡的优化轨迹。ARGUS具备动态特性，能实时响应突发情况，适应现代战场的不确定性。该系统的互操作性已在与葡萄牙陆军的实战演练中验证，证明其生成的路径可被UGV控制系统集成使用。结果表明，ARGUS不仅提供最优路径，还提供执行所需的关键洞察，显著提升自主地面系统作战效率与安全性。
原文摘要 · Abstract (English)
This thesis presents the development of ARGUS, a framework for mission planning for Unmanned Ground Vehicles (UGVs) in tactical environments. The system is designed to translate battlefield complexity and the commander's intent into executable action plans. To this end, ARGUS employs a processing pipeline that takes as input geospatial terrain data, military intelligence on existing threats and their probable locations, and mission priorities defined by the commander. Through a set of integrated modules, the framework processes this information to generate optimized trajectories that balance mission objectives against the risks posed by threats and terrain characteristics. A fundamental capability of ARGUS is its dynamic nature, which allows it to adapt plans in real-time in response to unforeseen events, reflecting the fluid nature of the modern battlefield. The system's interoperability were validated in a practical exercise with the Portuguese Army, where it was successfully demonstrated that the routes generated by the model can be integrated and utilized by UGV control systems. The result is a decision support tool that not only produces an optimal trajectory but also provides the necessary insights for its execution, thereby contributing to greater effectiveness and safety in the employment of autonomous ground systems.
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