Integrating path planning and task scheduling in autonomous drone operations - Ahmed KAMIL; Basim MAHMOOD (Applied Computer Science, 2025)
The efficiency and adaptability of drone operations depend heavily on two critical components: path planning and task scheduling. While the literature provides extensive research on these algorithms independently, there is a severe lack of studies addressing their combined impact on drone performance. Hence, this study aims to bridge this gap by developing a comprehensive framework that integrates three path planning algorithms (Spiral, Boustrophedon, and Hybrid) with four task scheduling algorithms (FirstCome First-Served (FCFS), Shortest Processing First (SPF), Earliest Deadline First (EDF), and Priority). The hybrid path planning algorithm is proposed for this work. The framework evaluates each combination's performance based on key metrics, including elapsed time and energy consumption. A virtual simulation environment is designed and implemented for the sake of this study. The results show that combining the SPF scheduling algorithm with Hybrid path planning offers the best balance between time efficiency and energy consumption. The Boustrophedon path planning method shows the highest elapsed times and is generally less efficient than Hybrid and Spiral.