{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/energy-aware-multi-uav-coverage-mission","title":"Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight","arxiv_id":"2402.10529","date":"2024-02-16","proceeding":null,"authors":["Denys Datsko","Frantisek Nekovar","Robert Penicka","Martin Saska"],"abstract":"This paper tackles the problem of planning minimum-energy coverage paths for multiple UAVs. The addressed Multi-UAV Coverage Path Planning (mCPP) is a crucial problem for many UAV applications such as inspection and aerial survey. However, the typical path-length objective of existing approaches does not directly minimize the energy consumption, nor allows for constraining energy of individual paths by the battery capacity. To this end, we propose a novel mCPP method that uses the optimal flight speed for minimizing energy consumption per traveled distance and a simple yet precise energy consumption estimation algorithm that is utilized during the mCPP planning phase. The method decomposes a given area with boustrophedon decomposition and represents the mCPP as an instance of Multiple Set Traveling Salesman Problem with a minimum energy objective and energy consumption constraint. The proposed method is shown to outperform state-of-the-art methods in terms of computational time and energy efficiency of produced paths. The experimental results show that the accuracy of the energy consumption estimation is on average 97% compared to real flight consumption. The feasibility of the proposed method was verified in a real-world coverage experiment with two UAVs.","url_abs":"https://arxiv.org/abs/2402.10529v1","url_pdf":"https://arxiv.org/pdf/2402.10529v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"energy-aware-multi-uav-coverage-mission","repo_url":"https://github.com/ctu-mrs/energyawaremcpp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}