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.
Flying a quadrotor through a cluttered environment requires not only planning a collision-free reference trajectory based on perceived obstacles, but the reference also needs to be dynamically feasible and within the actuation limits of the vehicle, so that the controller can track it precisely. Existing methods either...
Ondřej Procházka, Juraj Pauko, Robert Pěnička et al.· 0 citations
Rapidly capturing moving targets is critical for maintaining border security and public safety. However, traditional swarm-based target acquisition models often rely on the navigator’s observations and focus on fixed-formation capture methods. When target positions are uncertain and moving rapidly, deadlocks may occur...
Ping Zhang, Xin Yan, Hong-Wei Liu et al.· Intelligence & Robotics· 0 citations
Autonomous UAV flight through cluttered and partially unknown environments requires reasoning not only about observed obstacles but also about occluded regions that the sensor cannot observe. We present OA-MPPI, an obstacle- and occlusion-aware extension of Model Predictive Path Integral (MPPI) control for quadrotor fl...
Vittorio Palladino, Teaya Yang, Ruiqi Zhang et al.· 0 citations
Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume perfect tracking and remain blind to execution errors. To guarantee safety, trajectory planners therefore restrict themselves to conservative...
Ann-Kathrin Schwehn, Alexander Langmann, Mattia Piccinini et al.· 0 citations
Joint range and encounter-risk prediction can improve obstacle avoidance without retraining the locomotion policy, and results show that joint range and encounter-risk prediction can improve obstacle avoidance without retraining the locomotion policy.
Tian-Hao Zang, Shan-Ze Wang, Zi-Qian Wang et al.· 0 citations
PL-RiskPlanner is proposed, a constraint-consistent framework that applies asset- and obstacle-specific checks before admitting grid edges, ranks the admitted edges using a power-line risk field, predicts moving-object positions at estimated UAV arrival times, and applies the same sampled verifier during smoothing.
Cong Li, Yonggang Li, Lei Tan et al.· Electronics· 1 citation
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