Agile trajectory planning can improve the efficiency of multi-rotor Uncrewed Aerial Vehicles (UAVs) in scenarios with combined task-oriented and kinematic trajectory planning, such as monitoring spatio-temporal phenomena or intercepting dynamic targets. Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly computationally demanding. That reduces the prediction horizon length, leading to a decrease in solution quality. Besides, the fixed time-step length limits the utilization of the available UAV dynamics in the target neighborhood. In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length. A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the generation of feasible trajectories in the flat output space. Based on the presented evaluation results and experimentally validated deployment, the proposed method increases the solution quality by enabling planning for long flight segments but allowing tightly sampled maneuvering.

翻译：敏捷轨迹规划能够提升多旋翼无人机在兼具任务导向与运动学轨迹规划场景中的作业效率，例如时空现象监测或动态目标拦截。现有非线性模型预测控制方法在进行敏捷规划时受限于规划步数，其计算需求随步数增加而急剧增长，导致预测时域长度被迫缩短，进而降低求解质量。此外，固定时间步长限制了无人机在目标邻域内对可用动力学的充分利用。本文通过引入可变时间步长并将其与预测时域长度耦合，以解决上述局限性。研究采用简化的点质量运动基元，利用四旋翼动力学微分平坦特性，在平坦输出空间生成可行轨迹。基于所呈现的评估结果及实验验证部署，本方法通过支持长航段规划同时允许密集采样机动，有效提升了求解质量。