In this work, we aim to develop a fast trajectory replanning methodology enabling highly agile aerial vehicles to navigate in cluttered environments. By focusing on reducing complexity and accelerating the replanning problem under strict dynamical constraints, we employ the b-spline theory with local support property for defining the high dimensional agile flight trajectories. We utilize the differential flatness model of an aerial vehicle, allowing us to directly map the desired output trajectory into input states to track a high dimensional trajectory. Dynamically feasible replanning problem is addressed through regenerating the local b-splines with control point reallocation. As the geometric form of the trajectory based on the location of the control points and the knot intervals, the control point reallocation for fast replanning with dynamical constraints is turned into a constrained optimization problem and solved through deep reinforcement learning. The proposed methodology enables generating dynamically feasible local trajectory segments, which are continuous to the existing, hence provides fast local replanning for collision avoidance. The DRL agent is trained with different environmental complexities, and through the batch simulations, it is shown that the proposed methodology allows to solve fast trajectory replanning problem under given or hard dynamical constraints and provide real-time applicability for such collision avoidance applications in agile unmanned aerial vehicles. Hardware implementation tests of the algorithm with the agile trajectory tracker to a small UAV can bee seen in the following video link: https://youtu.be/8IiLQFQ3V0E.

在这项工作中，我们旨在开发一种快速的轨迹重新规划方法，以使高度敏捷的飞行器能够在混乱的环境中导航。通过专注于降低复杂性并在严格的动力学约束下加速重新规划问题，我们采用具有局部支持特性的b样条理论来定义高维敏捷飞行轨迹。我们利用飞行器的微分平坦度模型，允许我们将所需的输出轨迹直接映射到输入状态以跟踪高维轨迹。通过使用控制点重新分配来生成局部b样条，可以解决动态可行的重新计划问题。作为基于控制点位置和打结间隔的轨迹的几何形式，将具有动态约束的快速重新规划的控制点重新分配变成约束优化问题，并通过深度强化学习解决。所提出的方法使得能够生成与现有连续的动态可行的局部轨迹段，从而为避免碰撞提供快速的局部重新规划。对DRL代理进行了不同环境复杂性的训练，并通过批处理仿真表明，所提出的方法可以解决在给定的或严格的动态约束下的快速轨迹重新规划问题，并为敏捷无人驾驶中的此类防撞应用提供实时适用性飞机。在以下视频链接中可以看到使用敏捷轨迹跟踪器对小型无人机进行算法的硬件实施测试：