Multi-UAV system is widely used in surveillance, search and rescue, and industrial inspection. Multi-UAV trajectory planning is crucial for the multi-UAV system, but multi-UAV trajectory planning often needs to consider many constraints, such as trajectory smoothness, obstacle collisions, mutual collisions, dynamic limits, time-consuming, and trajectory length. It is a challenge to balance these constraints while considering computational performance. This paper proposes a novel multi-UAV trajectory planning method to solve the challenge. This method uses time segmentation instead of traditional waypoint segmentation to establish a trajectory optimization model based on the unified time interval, which simplifies the calculation of cost functions. At the same time, virtual segments are introduced to adapt to the trajectory length of different UAVs to reduce the total arrival time. Nonlinear constraints are cast into cost functions and a gradient-based sequential minimal optimization (GB-SMO) algorithm is proposed to minimize the cost function, which decouples the constraint of the mutual collisions in each iteration to save the planning time. Experiments are performed on a multi-UAV system to prove the effectiveness of the proposed method. Results show that this method has good performance in obstacle-rich environments and is efficient for a large number of UAVs.

Multi-UAV系统广泛用于监视，搜索和救援以及工业检查。多UAV轨迹规划对于多UAV系统至关重要，但是多UAV轨迹规划通常需要考虑许多约束条件，例如轨迹平滑度，障碍物碰撞，相互碰撞，动态限制，耗时和轨迹长度。在考虑计算性能的同时平衡这些约束是一个挑战。本文提出了一种新颖的多无人机航迹规划方法来解决这一挑战。该方法使用时间分割代替传统的航路点分割，基于统一的时间间隔建立轨迹优化模型，简化了成本函数的计算。与此同时，引入虚拟段以适应不同无人机的弹道长度，以减少总到达时间。将非线性约束转化为成本函数，并提出了一种基于梯度的顺序最​​小优化算法（GB-SMO）来最小化成本函数，该算法将每次迭代中相互冲突的约束解耦，从而节省了规划时间。在多无人机系统上进行了实验，以证明该方法的有效性。结果表明，该方法在障碍物丰富的环境中具有良好的性能，对大量的无人机有效。这样可以使每次迭代中相互冲突的约束解耦，从而节省了计划时间。在多无人机系统上进行了实验，以证明该方法的有效性。结果表明，该方法在障碍物丰富的环境中具有良好的性能，对大量的无人机有效。这样可以使每次迭代中相互冲突的约束解耦，从而节省了计划时间。在多无人机系统上进行了实验，以证明该方法的有效性。结果表明，该方法在障碍物丰富的环境中具有良好的性能，对大量的无人机有效。