{"ID":2887269,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01671","arxiv_id":"2508.01671","title":"Energy-Predictive Planning for Optimizing Drone Service Delivery","abstract":"We propose a novel Energy-Predictive Drone Service (EPDS) framework for efficient package delivery within a skyway network. The EPDS framework incorporates a formal modeling of an EPDS and an adaptive bidirectional Long Short-Term Memory (Bi-LSTM) machine learning model. This model predicts the energy status and stochastic arrival times of other drones operating in the same skyway network. Leveraging these predictions, we develop a heuristic optimization approach for composite drone services. This approach identifies the most time-efficient and energy-efficient skyway path and recharging schedule for each drone in the network. We conduct extensive experiments using a real-world drone flight dataset to evaluate the performance of the proposed framework.","short_abstract":"We propose a novel Energy-Predictive Drone Service (EPDS) framework for efficient package delivery within a skyway network. The EPDS framework incorporates a formal modeling of an EPDS and an adaptive bidirectional Long Short-Term Memory (Bi-LSTM) machine learning model. This model predicts the energy status and stocha...","url_abs":"https://arxiv.org/abs/2508.01671","url_pdf":"https://arxiv.org/pdf/2508.01671v1","authors":"[\"Guanting Ren\",\"Babar Shahzaad\",\"Balsam Alkouz\",\"Abdallah Lakhdari\",\"Athman Bouguettaya\"]","published":"2025-08-03T08:56:45Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.DC\",\"cs.ET\"]","methods":"[]","has_code":false}