# Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control

arXiv: https://arxiv.org/abs/2609.13270 · HTML: https://mlsanity.com/paper/2609.13270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories](https://mlsanity.com/paper/1712.02402/) (2017)
- [GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control](https://mlsanity.com/paper/2401.14554/) (2024)
- [Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning](https://mlsanity.com/paper/1909.05150/) (2019)
- [Accurate Tracking of Aggressive Quadrotor Trajectories using Incremental Nonlinear Dynamic Inversion and Differential Flatness](https://mlsanity.com/paper/1809.04048/) (2018)
- [Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions](https://mlsanity.com/paper/2105.10596/) (2021)
- [Reinforcement Learning of the Prediction Horizon in Model Predictive Control](https://mlsanity.com/paper/2102.11122/) (2021)
- [Adaptive Horizon Model Predictive Control](https://mlsanity.com/paper/1602.08619/) (2016)
- [Event-triggered and distributed model predictive control for guaranteed collision avoidance in UAV swarms](https://mlsanity.com/paper/2206.11020/) (2022)
- [Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version](https://mlsanity.com/paper/2210.10128/) (2022)
- [RL-based Variable Horizon Model Predictive Control of Multi-Robot Systems using Versatile On-Demand Collision Avoidance](https://mlsanity.com/paper/2308.07071/) (2023)
- [Distributed Model Predictive Control with Adaptive Safety Zones for Multi-Fleet Drone Operations](https://mlsanity.com/paper/2606.20651/) (2026)

## Abstract (arXiv)

In distributed model predictive control for multi-drone collision avoidance, a fixed prediction horizon forces a compromise: a short horizon is inexpensive but reacts late to approaching neighbors, whereas a long one anticipates conflicts at a per-step cost that grows superlinearly with its length. We propose a conflict-predictive variable horizon that each drone sets locally, leaving the distributed model predictive control itself unchanged. From a short history of observed positions, a drone extrapolates the flight lines of its neighbors, tests each against its own using confidence funnels that narrow with prediction range, and obtains each time to conflict in closed form. The horizon is then the smallest admissible value whose planning window covers the farthest predicted conflict. It collapses to its minimum in clear airspace and grows only when a conflict lies ahead. Provided this minimum meets a single computable feasibility bound, we prove that recursive feasibility and asymptotic stability are preserved for every horizon the policy can select. These guarantees hold for a linear model, and a cascaded inner loop reduces each quadrotor's translational dynamics to a perturbed double integrator, so they carry over to the linearized quadrotor model and, as practical stability, to the full nonlinear one. In simulation on dense antipodal-swap benchmarks, the variable horizon reduces both per-step solver cost and total computation well below those of a long fixed horizon, and it maintains separation in every run, which a short fixed horizon of comparable per-step cost does not.