{"paper":{"title":"Safe and Energy-Aware Decentralized PDE-Constrained Optimization-Based Control of Multi-UAVs for Persistent Wildfire Suppression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A decentralized optimization framework lets groups of UAVs suppress wildfires persistently while staying safe and managing energy under uncertainties.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Gennaro Notomista, Longchen Niu","submitted_at":"2026-05-12T21:48:36Z","abstract_excerpt":"This paper presents a safe and energy-aware optimization-based control framework for multi-UAV wildfire suppression under localization and motion uncertainties. We first develop a centralized density-based controller that couples UAV motion and water deployment in a wildfire-specific control Lyapunov function. This framework is then extended to a decentralized setting suitable for large-scale operations using only local information. The controllers use control barrier function constraints to enforce both danger zone avoidance and the ability to reach a charging region. Simulations and real qua"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Simulations and real quadcopter experiments demonstrate the controller's effectiveness in fire suppression while preserving safety and energy sufficiency over multiple charge cycles.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The wildfire can be accurately modeled via PDEs for density-based control, and localization/motion uncertainties remain bounded such that barrier functions can enforce safety and reachability constraints.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A decentralized optimization-based controller for multi-UAV wildfire suppression ensures safety and energy sufficiency using control Lyapunov and barrier functions under uncertainties.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A decentralized optimization framework lets groups of UAVs suppress wildfires persistently while staying safe and managing energy under uncertainties.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1b224a265beb674b3b17d581fa3c59815b7f50abd8b45f534a7e5b6bd24cb659"},"source":{"id":"2605.12779","kind":"arxiv","version":1},"verdict":{"id":"4d41f61c-6ea2-44eb-940b-4a09724e1165","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T19:29:54.302521Z","strongest_claim":"Simulations and real quadcopter experiments demonstrate the controller's effectiveness in fire suppression while preserving safety and energy sufficiency over multiple charge cycles.","one_line_summary":"A decentralized optimization-based controller for multi-UAV wildfire suppression ensures safety and energy sufficiency using control Lyapunov and barrier functions under uncertainties.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The wildfire can be accurately modeled via PDEs for density-based control, and localization/motion uncertainties remain bounded such that barrier functions can enforce safety and reachability constraints.","pith_extraction_headline":"A decentralized optimization framework lets groups of UAVs suppress wildfires persistently while staying safe and managing energy under uncertainties."},"references":{"count":28,"sample":[{"doi":"10.1073/pnas.1607171113","year":2016,"title":"2016 , title =","work_id":"859db496-e7be-4ba9-8004-98fae9381a41","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1016/j.cam","year":2013,"title":"Journal of Computational and Applied Mathemat- ics237(1), 487–507 (2013).https://doi.org/https://doi.org/10.1016/j.cam","work_id":"948ed9b1-c8b3-4803-b0f5-55dc96ca2849","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"Archer, A.J., Rauscher, M.: Dynamical density functional theory for interacting brownian particles: stochastic or deterministic? 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