面向安全无人机目标跟踪的未来感知流规划
Future-Aware Flow Planning for Safe UAV Target Following
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中文总结 AI 辅助
提出未来感知流规划框架，利用预测目标未来生成安全轨迹，并嵌入风险修复，在基准测试中改善安全-跟踪权衡，实现零碰撞率与高安全跟踪时间。
中文摘要 AI 辅助
在杂乱环境中，无人机目标跟踪本质上是预测性的：基于当前状态的跟随器可能滞后于转弯，选择被阻塞的走廊，或将跟踪性能牺牲给不安全的近地平线运动。我们提出了一种面向状态感知无人机目标跟踪的未来感知流规划框架。预测的目标未来作为水平对齐的残差信号引导干净的无人机轨迹生成，而风险评分的可执行前缀修复嵌入在采样循环内。在固定的ID/OOD滚动时域基准上，该规划器改善了预期的安全-跟踪权衡，而非在每个指标上占优：它匹配了零测量的ID碰撞率，同时具有最高的ID安全跟踪时间，并在所展示的方法中给出了最低的OOD宏观碰撞率和最终跟踪误差，而Future-MPC在其手工设计的目标下，在某些阈值化的OOD成功指标上仍更平滑且更强。消融研究表明，未来适应在安全修复之前改善了候选生成，而面向模拟器的压力测试探讨了接口、感知和控制器执行的影响。这些结果支持水平对齐的未来适应和嵌入式前缀修复作为在测试模拟条件下安全无人机目标跟踪的互补要素。
英文摘要
UAV target following in cluttered environments requires anticipating target motion. Followers that use only the current target state can lag behind turns or choose blocked corridors. They may also trade safe near-horizon motion for lower tracking error. We propose a future-aware flow planning framework for state-informed UAV target following. Predicted target futures guide clean UAV trajectory generation through residual signals aligned with the planning horizon. Risk-scored repair of the executable prefix is embedded in the sampling loop. On fixed in-distribution (ID) and out-of-distribution (OOD) receding-horizon benchmarks, the planner improves the safety--tracking trade-off. It matches zero measured ID collision rate and achieves the highest ID safe-tracking time fraction. It also gives the lowest OOD macro-average collision rate and final tracking error among the compared methods. It does not dominate every metric: Future-MPC remains smoother and stronger on some threshold-based OOD success metrics under its hand-designed objective. Controlled comparisons show that future conditioning with the adapter improves candidate generation before safety repair. Simulator-facing tests examine interface perturbations, sensing, and controller execution. These results support horizon-aligned future guidance and embedded prefix repair as complementary components for safe UAV target following under the tested simulation conditions.
发表机构
- Stockholm University（斯德哥尔摩大学）
- Peking University（北京大学）
- Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）
- Guangdong Provincial/Zhuhai Key Laboratory of IRADS（广东省/珠海市智能机器人先进感知与决策系统重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
