Impact-Aware Model Predictive Control for UAV Landing on a Heaving Platform
Robotics researchers solve the bouncy landing problem with impact-aware control...
Researchers Jess Stephenson and Melissa Greeff from Queen's University have developed an impact-aware Model Predictive Control (MPC) framework that dramatically improves UAV landing on heaving marine platforms. Their key innovation is modeling the landing event as a velocity-level rigid-body impact governed by Newton's restitution law, embedded as a linear complementarity problem (LCP) within the MPC dynamics. This allows the controller to predict the discontinuous post-impact velocity and actively suppress rebound, rather than reacting after the bounce.
In simulation, the restitution-aware prediction reduces pre-impact relative velocity and enhances landing robustness. Real-world experiments on a heaving-deck testbed demonstrate an 86.2% reduction in post-impact deflection compared to a standard tracking MPC. The work, to be published at the IFAC World Congress 2026, addresses a critical challenge for autonomous drone operations on ships, offshore platforms, and other moving surfaces where large impact forces can cause mission failure or damage.
- Uses Newton's restitution law modeled as an LCP within MPC dynamics to predict post-impact velocity
- Achieves 86.2% reduction in post-impact deflection on a physical heaving-deck testbed
- To be published at IFAC World Congress 2026, targeting autonomous marine UAV operations
Why It Matters
Enables safer, more reliable autonomous drone landings on ships and offshore platforms, critical for maritime logistics and surveillance.