Integrating path planning and task scheduling in autonomous drone operations

Ahmed KAMIL; Basim MAHMOOD

doi:10.35784/acs_6872

Integrating path planning and task scheduling in autonomous drone operations

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Published: Jun 28, 2025

DOI: https://doi.org/10.35784/acs_6872

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DOI

https://doi.org/10.35784/acs_6872

Authors

Ahmed KAMIL

ahmed.22csp21@student.uomosul.edu.iq

University of Mosul

https://orcid.org/0009-0005-8290-6969

Basim MAHMOOD

bmahmood@uomosul.edu.iq

University of Mosul

https://orcid.org/0000-0001-5070-1882

Abstract

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.

Keywords:

drone, UAV, path planning, task scheduling, complex networks

References

Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer communications, 149, 270-299. https://doi.org/10.1016/j.comcom.2019.10.014 DOI: https://doi.org/10.1016/j.comcom.2019.10.014

Alfathe, M., Azhar, A., Ali Othman, A. J., & Kashmola, M. (2025). Adopting complex networks to detect cheat cases in electronic exams. International Journal of Computing and Digital Systems, 17(1), 1-9. http://dx.doi.org/10.12785/ijcds/1571012424 DOI: https://doi.org/10.12785/ijcds/1571012424

Al-Kateeb, Z. N., & Abdullah, D. B. (2024). AdaBoost-powered cloud of things framework for low-latency, energy-efficient chronic kidney disease prediction. Transactions on Emerging Telecommunications Technologies, 35(6), e5007. https://doi.org/10.1002/ett.5007 DOI: https://doi.org/10.1002/ett.5007

Bähnemann, R., Lawrance, N., Chung, J. J., Pantic, M., Siegwart, R. & Nieto, J., 2021. Revisiting boustrophedon coverage path planning as a generalized traveling salesman problem. Field and Service Robotics: Results of the 12th International Conference (pp. 277-290). Springer Singapore. https://doi.org/10.1007/978-981-15-9460-1_20 DOI: https://doi.org/10.1007/978-981-15-9460-1_20

Chiang, H.-T., HomChaudhuri, B., Smith, L., & Tapia, L. (2020). Safety, challenges, and performance of motion planners in dynamic environments. In N. M. Amato, G. Hager, S. Thomas, & M. Torres-Torriti (Eds.), Robotics Research (Vol. 10, pp. 793–808). Springer International Publishing. https://doi.org/10.1007/978-3-030-28619-4_55 DOI: https://doi.org/10.1007/978-3-030-28619-4_55

Du, R., Zhao, T., Dong, X. & Zhu, R. (2022). September. An improved method for the UAV coverage path planning. 2022 4th International Conference on Robotics and Computer Vision (ICRCV) (pp. 325-328). IEEE. https://doi.org/10.1109/ICRCV55858.2022.9953213 DOI: https://doi.org/10.1109/ICRCV55858.2022.9953213

Duan, T., Wang, W., Wang, T., Chen, X., & Li, X. (2020). Dynamic tasks scheduling model of UAV cluster based on flexible network architecture. IEEE Access, 8, 115448–115460. https://doi.org/10.1109/ACCESS.2020.3002594 DOI: https://doi.org/10.1109/ACCESS.2020.3002594

Hadeed, W., & Abdullah, D. (2021). Real-time based big data and e-learning: A survey and open research issues. AL-Rafidain Journal of Computer Sciences and Mathematics, 15(2), 225–243. https://doi.org/10.33899/csmj.2021.170044 DOI: https://doi.org/10.33899/csmj.2021.170044

Hasan, M. Z., & Al‐Rizzo, H. (2019). Task scheduling in internet of things cloud environment using a robust particle swarm optimization. Concurrency and Computation: Practice and Experience, 32(2), e5442. https://doi.org/10.1002/cpe.5442 DOI: https://doi.org/10.1002/cpe.5442

Hassanalian, M., & Abdelkefi, A. (2017). Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, 91, 99-131. https://doi.org/10.1016/j.paerosci.2017.04.003 DOI: https://doi.org/10.1016/j.paerosci.2017.04.003

Hüppi, M., Bartolomei, L., Mascaro, R., & Chli, M. (2022). T-prm: Temporal probabilistic roadmap for path planning in dynamic environments. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 10320-10327). IEEE. https://doi.org/10.1109/IROS47612.2022.9981739 DOI: https://doi.org/10.1109/IROS47612.2022.9981739

Khosiawan, Y., Park, Y., Moon, I., Nilakantan, J. M., & Nielsen, I. (2019). Task scheduling system for UAV operations in indoor environment. Neural Computing and Applications, 31(9), 5431–5459. https://doi.org/10.1007/s00521-018-3373-9 DOI: https://doi.org/10.1007/s00521-018-3373-9

Liu, H., Tsang, Y. P. and Lee, C. K. M. (2024). A cyber-physical social system for autonomous drone trajectory planning in last-mile superchilling delivery. Transportation Research Part C: Emerging Technologies, 158, 104448. https://doi.org/10.1016/j.trc.2023.104448 DOI: https://doi.org/10.1016/j.trc.2023.104448

Liu, J. W. S. (2006). Real-time systems. Pearson Education.

Liu, S., Jin, Z., Lin, H., & Lu, H. (2024). An improve crested porcupine algorithm for UAV delivery path planning in challenging environments. Scientific Reports, 14, 20445. https://doi.org/10.1038/s41598-024-71485-1 DOI: https://doi.org/10.1038/s41598-024-71485-1

Mahmood, B., Sultan, N. A., Thanoon, K. H., & Khadhim, D. S. (2020). Collaboration networks: university of mosul case study. AL-Rafidain Journal of Computer Sciences and Mathematics, 14(1), 117-133. https://doi.org/10.33899/CSMJ.2020.164679 DOI: https://doi.org/10.33899/csmj.2020.164679

Mantoro, T., Alamsyah, Z., & Ayu, M. A. (2021). Pathfinding for disaster emergency route using Sparse A* and Dijkstra Algorithm with augmented reality. 2021 IEEE 7th International Conference on Computing, Engineering and Design (ICCED) (pp. 1-6). IEEE. https://doi.org/10.1109/icced53389.2021.9664869 DOI: https://doi.org/10.1109/ICCED53389.2021.9664869

Mao, X., Wu, G., Fan, M., Cao, Z., & Pedrycz, W. (2024). DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV. IEEE Transactions on Automation Science and Engineering, 22, 1028-1044. https://doi.org/10.1109/TASE.2024.3358894 DOI: https://doi.org/10.1109/TASE.2024.3358894

Mourya, G. S., Jami, A., Ajith, A., Seikh, A. H. & Kumar, J. S. (2024). Multi-Trajectory Drone surveillance: Amplifying monitoring capabilities in disaster management. 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE. https://doi.org/10.1109/SCEECS61402.2024.10482271 DOI: https://doi.org/10.1109/SCEECS61402.2024.10482271

Niu, Z., Liu, H., Lin, X., & Du, J. (2022). Task scheduling with UAV-assisted dispersed computing for disaster scenario. IEEE Systems Journal, 16(4), 6429–6440. https://doi.org/10.1109/JSYST.2021.3139993 DOI: https://doi.org/10.1109/JSYST.2021.3139993

Pasha, J., Elmi, Z., Purkayastha, S., Fathollahi-Fard, A. M., Ge, Y.-E., Lau, Y.-Y., & Dulebenets, M. A. (2022). The drone scheduling problem: A systematic state-of-the-art Review. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14224–14247. https://doi.org/10.1109/TITS.2022.3155072 DOI: https://doi.org/10.1109/TITS.2022.3155072

Qin, Z., Wang, H., Wei, Z., Qu, Y., Xiong, F., Dai, H., & Wu, T. (2021). Task selection and scheduling in UAV-Enabled MEC for reconnaissance with time-varying priorities. IEEE Internet of Things Journal, 8(24), 17290–17307. https://doi.org/10.1109/JIOT.2021.3078746 DOI: https://doi.org/10.1109/JIOT.2021.3078746

Reda, M., Onsy, A., Haikal, A. Y., & Ghanbari, A. (2024). Path planning algorithms in the autonomous driving system: A comprehensive review. Robotics and Autonomous Systems, 174, 104630. https://doi.org/10.1016/j.robot.2024.104630 DOI: https://doi.org/10.1016/j.robot.2024.104630

Sanders, A. (2016). An introduction to Unreal engine 4. AK Peters/CRC Press. DOI: https://doi.org/10.1201/9781315382555

Shah, S., Dey, D., Lovett, C. & Kapoor, A. (2018). Airsim: High-fidelity visual and physical simulation for autonomous vehicles. Field and Service Robotics: Results of the 11th International Conference (pp. 621-635). Springer International Publishing. https://doi.org/10.1007/978-3-319-67361-5_40 DOI: https://doi.org/10.1007/978-3-319-67361-5_40

Sung, I., Danancier, K., Delphine Ruvio, D., Guillemet, A., & Nielsen, P. (2019). A design of a scheduling system for an unmanned aerial vehicle (UAV) deployment. IFAC-PapersOnLine, 52(13), 1854-1859. https://doi.org/10.1016/j.ifacol.2019.11.472 DOI: https://doi.org/10.1016/j.ifacol.2019.11.472

Wang, Z., Zhang, B., Xiang, Y., & Li, C. (2023). Joint task assignment and path planning for truck and drones in mobile crowdsensing. Peer-to-Peer Networking and Applications, 16, 1668-1679. https://doi.org/10.1007/s12083-022-01389-2 DOI: https://doi.org/10.1007/s12083-022-01389-2

Yang, W., Mao, Y., Chen, X., Chen, C., Lei, B., & He, Q. (2025). A method for simultaneously implementing trajectory planning and DAG task scheduling in multi-UAV assisted edge computing. Ad Hoc Networks, 166, 103668. https://doi.org/10.1016/j.adhoc.2024.103668 DOI: https://doi.org/10.1016/j.adhoc.2024.103668

You, W., Dong, C., Wu, Q., Qu, Y., Wu, Y., & He, R. (2022). Joint task scheduling, resource allocation, and UAV trajectory under clustering for FANETs. China Communications, 19(1), 104–118. https://doi.org/10.23919/JCC.2022.01.009 DOI: https://doi.org/10.23919/JCC.2022.01.009

Zhang, N., Zhang, M. and Low, K. H. (2021). 3D path planning and real-time collision resolution of multirotor drone operations in complex urban low-altitude airspace. Transportation Research Part C: Emerging Technologies, 129, 103123. https://doi.org/10.1016/j.trc.2021.103123 DOI: https://doi.org/10.1016/j.trc.2021.103123

Zhang, Z., Wu, J., Dai, J., & He, C., (2022). Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(1), 72-81. https://doi.org/10.1177/09544100211007381 DOI: https://doi.org/10.1177/09544100211007381

Article Details

KAMIL, A., & MAHMOOD, B. (2025). Integrating path planning and task scheduling in autonomous drone operations. Applied Computer Science, 21(2), 1–17. https://doi.org/10.35784/acs_6872

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