Impact-Aware Model Predictive Control for UAV Landing on a Heaving Platform

TL;DR

This paper introduces an impact-aware MPC framework for UAV landings on moving platforms, modeling impacts with Newton's law and embedding it as an LCP to improve landing robustness.

Contribution

It presents a novel impact-aware MPC that predicts impact dynamics and reduces rebound, validated through simulations and real-world experiments.

Findings

Restitution-aware prediction reduces pre-impact velocity.

Landing robustness is improved with the impact-aware MPC.

Experiments show an 86.2% reduction in post-impact deflection.

Abstract

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.