Energy-Predictive Planning for Optimizing Drone Service Delivery
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918112450838528 |
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| author | Ren, Guanting Shahzaad, Babar Alkouz, Balsam Lakhdari, Abdallah Bouguettaya, Athman |
| author_facet | Ren, Guanting Shahzaad, Babar Alkouz, Balsam Lakhdari, Abdallah Bouguettaya, Athman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01671 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy-Predictive Planning for Optimizing Drone Service Delivery Ren, Guanting Shahzaad, Babar Alkouz, Balsam Lakhdari, Abdallah Bouguettaya, Athman Robotics Distributed, Parallel, and Cluster Computing Emerging Technologies 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. |
| title | Energy-Predictive Planning for Optimizing Drone Service Delivery |
| topic | Robotics Distributed, Parallel, and Cluster Computing Emerging Technologies |
| url | https://arxiv.org/abs/2508.01671 |