As UAV popularity soars, so does the mission planning associated with it. Classical planning approaches suffer from the triple problems of decoupling of task assignment and path planning, poor real-time and scalability, and limited adaptability. Aiming at these challenges, this paper proposes a multi-UAV real-time collaborative mission planning method based on UAV states. First, the employed Dubins path accurately represents the distance between tasks and satisfies curvature constraints without smoothing, thus achieving a coupled solution for task assignment and path planning. Then, a series of acceleration techniques are applied to guarantee the real-time performance of the method, including task clustering to reduce the decision space, allocation strategies with fewer iterations, and efficient distance cost calculation methods. To enhance robustness and adaptability, real-time assignment of new tasks and task reassignment due to the reduction of available UAVs are appropriately handled. Finally, simulations highlight that the proposed method only increases the path length by 9.57% compared to benchmark method, while achieving a 4–5 orders-of-magnitude improvement in planning speed, with a single mission planning of about 0.0003 s. Moreover, it easily scales to large-scale scenarios (0.0029 s, with 1000 UAVs and 25,000 tasks).
Liu et al. (2026) studied this question.