{"ID":5438808,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T10:35:20.036867845Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31487","arxiv_id":"2606.31487","title":"Energy-Optimal Spatial Iterative Learning within a Virtual Tube","abstract":"Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on accurate system models and computationally expensive optimization procedures. This paper proposes a model-free online iterative learning (IL) framework to minimize energy consumption. Without requiring explicit models of UAV dynamics or energy consumption, the proposed method improves energy efficiency while maintaining a low computational cost. The per-iteration computational complexity is O(n), where n denotes the number of path points. In the tested cases, the proposed method is approximately 50--60 times faster than the model-based IPOPT benchmark. Simulation results and real-world flight experiments across multiple UAV platforms validate the effectiveness, computational efficiency, and practical applicability of the proposed approach.","short_abstract":"Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned aerial vehicles (UAVs) are severely constrained. Although energy-efficient trajectory planning and control have been widely studied, most existing approaches rely on ac...","url_abs":"https://arxiv.org/abs/2606.31487","url_pdf":"https://arxiv.org/pdf/2606.31487v1","authors":"[\"Chen Min\",\"Shuli Lv\",\"Pengda Mao\",\"Huixin Cao\",\"Li Hong\",\"Quan Quan\"]","published":"2026-06-30T11:01:06Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}