UAV Trajectory Planning under Time-Optimality and Field-of-View Constraints

With the widespread use of quadrotors in logistics, search and rescue, and other fields, online trajectory planning faces dual challenges: limited onboard computational resources and the need for real-time responses to dynamic environments. Building on the EGO-Planner, this study retains its advantages while incorporating time optimization, constraints on the field of view (FOV), and yaw planning, thus enhancing the algorithm's robustness in unknown environments. The key contributions of this study are as follows: First, a time penalty term is added to the objective function to optimize the overall task duration. Second, a geometric visibility model is developed that directly integrates FOV constraints into the trajectory generation process. Finally, a yaw planning method is proposed to mitigate high-frequency oscillations. Simulation results demonstrate that the improved algorithm significantly outperforms the classical EGO-Planner in terms of flight time efficiency, vertical obstacle avoidance success rate, and yaw smoothness.