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[Submitted on 7 Sep 2026] Title:Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control View a PDF of the paper titled Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control, by Linda M\"{u}mken and Michael Schwung and Stefan Lier and Andreas Schwung View PDF HTML (experimental) Abstract: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. Comments: 14 pages, 6 figures, 2 tables, is already submitted to IEEE Transactions on Systems, Man, and Cybernetics as journal-paper (current status "under review"). I think this is again more eess.SY, but also if my last paper is published there, i am still not allowed to use that category, please decide for yourself Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) Cite as: arXiv:2609.13270 [cs.RO] (or arXiv:2609.13270v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.13270 arXiv-issued DOI via DataCite (pending registration) Submission history From: Linda Mümken [view email] [v1] Mon, 7 Sep 2026 17:24:30 UTC (850 KB) Full-text links: Access Paper: View a PDF of the paper titled Conflict-Predictive Variable Horizons in Multi-Drone Distributed Model Predictive Control, by Linda M\"{u}mken and Michael Schwung and Stefan Lier and Andreas Schwung View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs cs.AI cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)