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.
This work presents a motion planning framework for UAV navigation in non-convex urban air corridors. The planner is based on a mixed-integer tracking model predictive control formulation that enforces corridor feasibility and dynamic consistency within a single optimization problem. To guarantee convergence to the target and mitigate the occurrence of local minima induced by non-convex geometry, a shortest-path-based offset cost with feasibility constraints is embedded directly into the planning problem. Numerical simulations show that the proposed formulation generates dynamically valid trajectories that satisfy the corridor constraints and converge to the target without relying on external global planning stages.
Henrique Silva, Marcelo A. Santos, Guilherme V. Raffo
Agile unmanned aerial vehicle (UAV) navigation in cluttered environments demands a planning architecture that is both computationally efficient and structurally expressive enough to reason over multiple feasible motions. This paper presents SAGA, a robust self-attention and goal-aware anchor-based planner for safe UAV autonomous navigation. SAGA formulates local planning as a one-stage joint regression-and-ranking problem over a fixed lattice of motion anchors. Given a depth image and a body-frame motion state, the planner predicts refined terminal states and planning scores for all anchors in a single forward pass, after which the best candidate is decoded into a dynamically feasible trajectory. The key idea of SAGA is to transform anchor-aligned features into geometry-aware tokens and perform cross-anchor global reasoning with self-attention. To preserve directional structure in the token space, we further introduce a polar positional encoding derived from anchor yaw and pitch. In addition, a goal-aware modulation module injects velocity, acceleration, and target information into the token representation before final score prediction. Experiments in cluttered pillar-map environments under maximum speed settings of 2.0, 3.0, and 4.0m/s show that SAGA consistently achieves a 100% success rate, while YOPO drops from 90.91% to 62.50%, Ego-planner from 71.43% to 52.63%, and Fast-planner from 52.63% to 38.46%. Under the 4.0m/s maximum speed setting, SAGA also improves average safety from 1.9843m to 2.3888m and minimum safety from 0.4390m to 0.7576m over YOPO, while reducing total flight time from 40.4631s to 27.4901s. The comparison with SAGA w/o PPE further shows that explicit polar positional encoding is critical for stable cross-anchor reasoning and safe passage selection in cluttered scenes.
This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-UAV trajectory design from a repeated combinatorial optimization problem into a hierarchical probabilistic inference problem. In the offline phase, a genetic-algorithm planner with repulsive-force collision avoidance (GA--RF) generates expert demonstrations, which are abstracted into Mission, Route, and Motion dictionaries. These dictionaries are used to learn a probabilistic world model that captures how expert mission allocations induce route orders and how route orders induce motion-level behaviors. During online operation, the UAV swarm evaluates candidate actions by forming posterior beliefs over symbolic states and minimizing KL-divergence-based abnormality indicators with respect to expert-derived reference distributions. This enables mission allocation, route insertion, motion adaptation, and collision-aware replanning without rerunning the offline optimizer. Bayesian state estimators, including EKF and PF modules, are integrated at the motion level to improve trajectory correction under uncertainty. Simulation results show that the proposed framework preserves expert-like planning structure while producing smoother and more stable behavior than modified Q-learning. Additional validation using real-flight UAV trajectory data demonstrates that the learned world model can correct symbolic predictions under noisy and non-smooth observations, supporting its applicability to adaptive UAV swarm autonomy.