An Improved Hierarchical Framework for Adaptive UAV Trajectory Planning in Dynamic Target Tracking

Planning trajectories for unmanned aerial vehicles (UAVs) actively tracking a moving target poses a significant challenge. This paper introduces a motion planning system utilizing UAVs to track a dynamic target actively. The system can handle complex tracking tasks, ensuring safety and dynamic feasibility. Initially, a front-end path search involves employing adaptive Kinodynamic path searching to identify the route. A heuristic approach is applied to determine an initial trajectory, considering the minimum time and control cost. Subsequently, we introduce the cost of combining optimization and use Euclidean Signed Distance Field (ESDF) gradient information to improve trajectory smoothness and dynamic feasibility. Simulation results confirm the consistent superiority of our proposed method over the Fast-Tracker algorithm, resulting in shorter tracking distance and flight time. Our method achieves smoother trajectories while preserving dynamic feasibility. A real-world single-UAV experiment further validates the effectiveness of our approach.