动态目标拦截中，可变时间步长提升无人机轨迹规划效率与精度。
Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets
- 采用可变时间步长与预测时域耦合，优化计算资源分配。
- 支持长段飞行规划与近目标区域高密度采样，提升轨迹可行性。
- 基于四旋翼微分平坦性，在平展空间生成可执行轨迹，适合高速拦截任务。
敏捷轨迹规划能提升多旋翼无人机在任务导向与运动学联合规划场景下的效率，如监测时空现象或拦截动态目标。现有非线性模型预测控制方法受限于规划步数，随步数增加计算开销剧增，导致预测时域缩短，降低解的质量。此外，固定时间步长限制了无人机在目标邻近区域的动态利用。本文提出引入可变时间步长，并将其与预测时域长度耦合，以缓解上述问题。采用简化点质量运动基元，利用四旋翼动力学的微分平坦性，在平展输出空间生成可行轨迹。实验验证表明，该方法在保证长飞行段规划能力的同时，实现近目标区域紧密采样的机动能力，显著提升解的质量。
原文摘要 · Abstract (English)
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.
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