# Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets

arXiv: https://arxiv.org/abs/2503.14184 · HTML: https://mlsanity.com/paper/2503.14184/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles](https://mlsanity.com/paper/2008.08050/) (2020)
- [Time-Optimal Online Replanning for Agile Quadrotor Flight](https://mlsanity.com/paper/2203.09839/) (2022)
- [Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer](https://mlsanity.com/paper/1703.01416/) (2017)
- [Perception-aware time optimal path parameterization for quadrotors](https://mlsanity.com/paper/2005.13986/) (2020)
- [Kinematic Orienteering Problem With Time-Optimal Trajectories for Multirotor UAVs](https://mlsanity.com/paper/2211.16837/) (2022)
- [MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments](https://mlsanity.com/paper/2302.04778/) (2023)
- [Multi-vehicle Dynamic Water Surface Monitoring](https://mlsanity.com/paper/2302.11991/) (2023)

## Built on by (most cited first, 2 of 2)

- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games](https://mlsanity.com/paper/2506.02849/) (2025)

## Abstract (arXiv)

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.