AI 中文总结
AgenticSwarm提出智能体框架，融合语义感知与约束分配，实现异构多无人机自适应任务执行，显著提升感知精度并降低重复工作。
AI 中文摘要
复杂环境中的多无人机任务要求系统既能理解周围场景，又能理解操作员的意图，同时在任务条件变化时保持可行的任务分配。本文提出了AgenticSwarm，一个用于异构多无人机任务中语义感知和自适应任务分配的智能体框架。一个智能体解释航空图像和自然语言指令，构建一个接地任务表示，将感知到的对象和区域与任务需求、能力约束和任务依赖关系联系起来。该信息增强了受约束的任务分配过程，在分配前纳入障碍物感知的路径可行性、能量消耗和受保护的返航要求。在执行过程中，无人机故障、电池退化或任务修改等变化会触发从当前系统状态进行残余任务重建，同时保留已完成的工作和侦察进度。AgenticSwarm在五个不同的Gazebo环境和一个室内真实测试环境中进行了评估，展示了其将语义推理与受约束分配和自适应多无人机任务执行联系起来的能力。与Grounding DINO+SAM~2.1感知基线相比，基于SAM3的流程将类别感知召回率提高了25.2个百分点（pp），语义标签准确率提高了29.5个百分点。消融残余任务重规划使平均重复工作从0%增加到61.7%，事件后恢复时间增加了58.6%，突出了自适应重规划对任务执行的贡献。
英文摘要
Multi UAV missions in complex environments require the system to understand both the surrounding scene and the intent of a human operator while maintaining feasible task allocation as mission conditions change. This paper presents AgenticSwarm, an agentic framework for semantic perception and adaptive task allocation in heterogeneous multi UAV missions. An agent interprets aerial imagery and natural language instructions to construct a grounded mission representation that links perceived objects and regions with task requirements, capability constraints, and mission dependencies. This information augments a constrained task allocation process in which obstacle aware path feasibility, energy consumption, and protected return home requirements are incorporated before assignment. During execution, changes such as UAV failure, battery degradation, or task modification trigger residual mission reconstruction from the current system state, while completed work and reconnaissance progress are retained. AgenticSwarm is evaluated across five diverse Gazebo environments and an indoor real test environment, demonstrating its ability to connect semantic reasoning with constrained allocation and adaptive multi UAV mission execution. Compared with a Grounding DINO+SAM~2.1 perception baseline, the SAM3-based pipeline improves class-aware recall by 25.2 percentage points (pp) and semantic label accuracy by 29.5 pp. Ablating residual mission replanning increases mean repeated work from 0% to 61.7% and post-event recovery time by 58.6%, highlighting the contribution of adaptive replanning to mission execution.
