Future-Aware Flow Planning for Safe UAV Target Following

2026-09-09

AI Summary

论文提出一种未来感知的流动规划框架，通过预测目标未来路径和风险评分前缀修复方法提高无人机在复杂环境中的安全跟踪性能。

Abstract

UAV target following in cluttered environments is inherently predictive: current-state followers can lag behind turns, choose blocked corridors, or trade tracking for unsafe near-horizon motion. We propose a future-aware flow planning framework for state-informed UAV target following. Predicted target futures guide clean UAV trajectory generation as horizon-aligned residual signals, while risk-scored executable-prefix repair is embedded inside the sampling loop. On fixed ID/OOD receding-horizon benchmarks, the planner improves the intended safety--tracking trade-off rather than dominating every metric: it matches zero measured ID collision rate with the highest ID safe-tracking time, and gives the lowest OOD macro collision rate and final tracking error among the displayed methods, while Future-MPC remains smoother and stronger on some thresholded OOD success metrics under its hand-designed objective. Ablations show that future adaptation improves candidate generation before safety repair, and simulator-facing stress tests probe interface, sensing, and controller-execution effects. These results support horizon-aligned future adaptation and embedded prefix repair as complementary ingredients for safe UAV target following under the tested simulation conditions.

Problem

• UAV
• target following
• cluttered environments
• safety
• tracking

Innovation

• future-aware flow planning
• horizon-aligned residual signals
• risk-scored executable-prefix repair
• UAV target following