让无人机在晃动甲板上降落时减少冲击力和反弹，提升稳定性。
Impact-Aware Model Predictive Control for UAV Landing on a Heaving Platform
- 用物理模型预测撞击后速度变化，抑制反弹。
- 实验显示撞击后偏移量降低86.2%。
- 适合海上无人机着陆场景的控制优化。
在晃动的海上平台着陆无人机具有挑战性，因相对垂直运动可能产生巨大冲击力并导致触地反弹。为此，我们提出一种考虑冲击影响的模型预测控制（MPC）框架，将着陆建模为基于牛顿恢复系数的速度假设刚体撞击问题，并将其作为线性互补问题（LCP）嵌入MPC动态中，以预测不连续的撞击后速度并抑制反弹。仿真结果表明，考虑恢复系数的预测可降低触地前的相对速度，提升着陆鲁棒性。在晃动甲板测试平台上进行的实验显示，相比传统跟踪MPC，撞击后的偏移量减少了86.2%。
原文摘要 · Abstract (English)
Landing UAVs on heaving marine platforms is challenging because relative vertical motion can generate large impact forces and cause rebound on touchdown. To address this, we develop an impact-aware Model Predictive Control (MPC) framework that models landing as a velocity-level rigid-body impact governed by Newton's restitution law. We embed this as a linear complementarity problem (LCP) within the MPC dynamics to predict the discontinuous post-impact velocity and suppress rebound. In simulation, restitution-aware prediction reduces pre-impact relative velocity and improves landing robustness. Experiments on a heaving-deck testbed show an 86.2% reduction in post-impact deflection compared to a tracking MPC.
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