Energy-Predictive Planning for Optimizing Drone Service Delivery

TL;DR

This paper introduces an Energy-Predictive Drone Service framework that uses machine learning to forecast drone energy and arrival times, optimizing routes and recharging schedules for efficient package delivery in a skyway network.

Contribution

It presents a novel framework combining formal modeling and Bi-LSTM predictions to optimize drone delivery routes and recharging strategies.

Findings

The framework effectively predicts drone energy and arrival times.

Optimized routes improve delivery efficiency and energy usage.

Experimental results validate the framework's performance on real-world data.

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.