通过预测无人机能耗与到达时间，优化空路配送路径与充电计划。
Energy-Predictive Planning for Optimizing Drone Service Delivery
- 用双向LSTM预测无人机能耗和到达时间
- 设计启发式算法找到最优飞行路径与充电方案
- 适用于大规模无人机配送系统调度
我们提出一种新型的能源预测无人机服务（EPDS）框架，用于在空路网络中高效完成包裹配送。该框架包含对EPDS的形式化建模，以及一个自适应的双向长短期记忆（Bi-LSTM）机器学习模型，可预测同一空路网络中其他无人机的能源状态和随机到达时间。基于这些预测，我们开发了一种启发式优化方法，为网络中每架无人机确定最省时且最节能的空路路径与充电调度方案。我们利用真实无人机飞行数据集进行了大量实验，评估所提框架的性能。
原文摘要 · Abstract (English)
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.
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