{"ID":2840504,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13188","arxiv_id":"2511.13188","title":"Collision-Free Navigation of Mobile Robots via Quadtree-Based Model Predictive Control","abstract":"This paper presents an integrated navigation framework for Autonomous Mobile Robots (AMRs) that unifies environment representation, trajectory generation, and Model Predictive Control (MPC). The proposed approach incorporates a quadtree-based method to generate structured, axis-aligned collision-free regions from occupancy maps. These regions serve as both a basis for developing safe corridors and as linear constraints within the MPC formulation, enabling efficient and reliable navigation without requiring direct obstacle encoding. The complete pipeline combines safe-area extraction, connectivity graph construction, trajectory generation, and B-spline smoothing into one coherent system. Experimental results demonstrate consistent success and superior performance compared to baseline approaches across complex environments.","short_abstract":"This paper presents an integrated navigation framework for Autonomous Mobile Robots (AMRs) that unifies environment representation, trajectory generation, and Model Predictive Control (MPC). The proposed approach incorporates a quadtree-based method to generate structured, axis-aligned collision-free regions from occup...","url_abs":"https://arxiv.org/abs/2511.13188","url_pdf":"https://arxiv.org/pdf/2511.13188v1","authors":"[\"Osama Al Sheikh Ali\",\"Sotiris Koutsoftas\",\"Ze Zhang\",\"Knut Akesson\",\"Emmanuel Dean\"]","published":"2025-11-17T09:51:32Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}