Edge-Assisted Multi-Agent Reinforcement Learning for Decentralized Autonomous Swarm Drone Navigation under Bandwidth Constraints

Lina Amira Becker; Aisha Lena Müller; Freya Amina Hansen; Aisha Laila Jørgensen; Oscar Yusuf Pedersen; Jakub Ali Wisniewski; Maja Hana Kaczmarek

doi:10.23917/saintek.v3i1.20053

Authors

Lina Amira Becker Faculty of Electrical Engineering, Computer Science and Mathematics, Universität Paderborn
Germany
Aisha Lena Müller Faculty of Electrical Engineering, Computer Science and Mathematics, Universität Paderborn
Germany
Freya Amina Hansen Faculty of Science, University of Copenhagen
Denmark
Aisha Laila Jørgensen Faculty of Science, University of Copenhagen
Denmark
Oscar Yusuf Pedersen Department of Computer Science, University of Southern Denmark
Denmark
Jakub Ali Wisniewski Faculty of Science and Technology, University of Silesia in Katowice
Poland
Maja Hana Kaczmarek Faculty of Science and Technology, University of Silesia in Katowic
Poland

DOI:

https://doi.org/10.23917/saintek.v3i1.20053

Keywords:

unmanned aerial vehicle swarm, multi-agent reinforcement learning, edge computing, decentralized navigation, bandwidth-aware communication, autonomous drone, swarm intelligence

Abstract

Decentralized navigation and inter-agent communication are key requirements for the safety of UAV swarms. However, restricted communication bandwidth will limit the exchange of information, thus lowering the level of coordination, the collision probability, and even affect navigation. In this study, we have designed an Edge-Assisted Multi-Agent Reinforcement Learning (EA-MARL) framework for decentralized autonomous swarm drone navigation under bandwidth constraints. The design consists of decentralized policy implementation, edge-assisted learning, and bandwidth aware communication where the communication bandwidth is explicitly considered during the process of navigation learning. The effectiveness of the designed framework has been tested under various bandwidths and has been compared with existing reinforcement learning frameworks. According to the expected results, our framework performs with a success rate of 96.1%, a collision rate of 2.8%, an average path length of 107.6 units and an average reward of 93.5. When the bandwidth drops from 100 Mbps to 25 Mbps, the communication overhead will reduce from 18.5 MB to 10.7 MB per episode with a drop of 42.2%, while the navigation success still maintains 89.6%. Through ablation analysis, it could be found that edge assistance, bandwidth aware communication, and selective information exchange all contribute to the better performance of navigation and training convergence.

Downloads

Download data is not yet available.

References

[1] K. Amer, M. Samy, M. Shaker, and M. Elhelw, “Deep convolutional neural network based autonomous drone navigation,” in Thirteenth International Conference on Machine Vision, W. Osten, J. Zhou, and D. P. Nikolaev, Eds., SPIE, Jan. 2021, p. 46. doi: 10.1117/12.2587105.

[2] A. M, A. K. A, A. B. Francis, K. Mohan, M. R, and M. Samshad, “Short Range Telemetry Communication for Autonomous Drone Navigation,” in 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS), IEEE, Dec. 2020, pp. 131–135. doi: 10.1109/RAICS51191.2020.9332518.

[3] S. Qamar, M. Qamar, M. Shahbaz, M. Arif Arshad, N. Saher Shah, and A. Khan, “Autonomous Drone Swarm Navigation in Complex Environments,” in 2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST), IEEE, Aug. 2022, pp. 290–295. doi: 10.1109/IBCAST54850.2022.9990563.

[4] S. S, K. N, S. K. S, C. Selvaganesan, B. Sundarambal, and S. K. G, “Autonomous Grievance Redressal System for Government Services,” in 2025 International Conference on Data Science and Business Systems (ICDSBS), IEEE, Apr. 2025, pp. 1–8. doi: 10.1109/ICDSBS63635.2025.11031909.

[5] V. R. F. Miranda, A. M. C. Rezende, T. L. Rocha, H. Azpúrua, L. C. A. Pimenta, and G. M. Freitas, “Autonomous Navigation System for a Delivery Drone,” Journal of Control, Automation and Electrical Systems, vol. 33, no. 1, pp. 141–155, Feb. 2022, doi: 10.1007/s40313-021-00828-4.

[6] A. Tyagi, C. Singhal, A. Kushwaha, A. Tiwari, and A. Rajak, “FlyDrop: Markerless Navigation for Autonomous Drones,” in 2025 Modern Electronics Devices and Intelligent Communication Systems (MEDCOM), IEEE, Dec. 2025, pp. 206–209. doi: 10.1109/MEDCOM67532.2025.11405093.

[7] F. G. Pradana and T. Dipsatara, “Utilization of Blockchain-Based Smart Contracts in Banking: A Systematic Review of Technical, Regulatory, and Systemic Risk Dimensions,” Jurnal Penelitian Sains Teknologi, pp. 91–104, Mar. 2026, doi: 10.23917/saintek.v2i2.16341.

[8] I. Ashwin, M. Rajagopal, M. Naren, V. S. Narayan, and B. Murugan, “Autonomous systems: Indoor drone navigation,” in Aip Conference Proceedings, 2024, p. 020002. doi: 10.1063/5.0226678.

[9] M. J. Kumar, S. Singh, A. Saneen, and D. Thomas, “Digital Twin for Drone Indoor Autonomous Navigation,” IEEE Sens. Lett., vol. 10, no. 2, pp. 1–4, Feb. 2026, doi: 10.1109/LSENS.2025.3642424.

[10] D. Hulens, W. Van Ranst, Y. Cao, and T. Goedemé, “Autonomous Visual Navigation for a Flower Pollination Drone,” Machines, vol. 10, no. 5, p. 364, May 2022, doi: 10.3390/machines10050364.

[11] A. Jufriansah et al., “NuminaMath 7B: Revolutionizing Math Solving with Integrated Reasoning Advanced Generative AI Tools and Python REPL,” Jurnal Penelitian Sains Teknologi, pp. 1–19, Feb. 2026, doi: 10.23917/saintek.v2i1.15728.

[12] M. S. Adhantoro, F. A. Haq, D. Hartanto, and A. P. Sudaryanto, “IndoBERT-Based Sentiment Analysis of Electric Motorcycle Policy in Indonesia Using Instagram Data,” Jurnal Penelitian Sains Teknologi, pp. 165–181, Apr. 2026, doi: 10.23917/saintek.v2i2.17021.

[13] A. B. Pulungan, Z. Y. Putra, A. R. Sidiqi, H. Hamdani, and K. E. Parigalan, “Drone Kit-Python for Autonomous Quadcopter Navigation,” JOIV : International Journal on Informatics Visualization, vol. 8, no. 3, p. 1287, Sep. 2024, doi: 10.62527/joiv.8.3.2301.

[14] S. Ikli and I. Quenel, “Autonomous Drone Takeoff and Navigation Using Reinforcement Learning,” in Proceedings of the 16th International Conference on Agents and Artificial Intelligence, SCITEPRESS - Science and Technology Publications, 2024, pp. 63–71. doi: 10.5220/0012296300003636.

[15] A. A. Rodriguez, M. Shekaramiz, and M. A. S. Masoum, “Computer Vision-Based Path Planning with Indoor Low-Cost Autonomous Drones: An Educational Surrogate Project for Autonomous Wind Farm Navigation,” Drones, vol. 8, no. 4, p. 154, Apr. 2024, doi: 10.3390/drones8040154.

[16] P.-C. Wu, Y.-Y. Chen, C.-L. Lin, and H. Wang, “Autonomous Localization and Navigation for Drones Over Rivers,” IEEE Access, vol. 14, pp. 5236–5254, 2026, doi: 10.1109/ACCESS.2025.3650454.

[17] J. N. Yasin, S. A. S. Mohamed, M.-H. Haghbayan, J. Heikkonen, H. Tenhunen, and J. Plosila, “Navigation of Autonomous Swarm of Drones Using Translational Coordinates,” in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 12092, 2020, pp. 353–362. doi: 10.1007/978-3-030-49778-1_28.

[18] M. G. Zaki and H. Imaduddin, “Early Classification of Diabetes Risk in Productive Age Groups Using Machine Learning,” Jurnal Penelitian Sains Teknologi, pp. 75–90, Feb. 2026, doi: 10.23917/saintek.v2i1.16430.

[19] R. Casado and A. Bermúdez, “A Simulation Framework for Developing Autonomous Drone Navigation Systems,” Electronics (Basel)., vol. 10, no. 1, p. 7, Dec. 2020, doi: 10.3390/electronics10010007.

[20] P. V. Hegde, M. Riyaz Ahmed, and A. Haq Nalband, “Memory-Efficient Annotation Techniques for Autonomous Drone Navigation,” IEEE Access, vol. 13, pp. 130846–130871, 2025, doi: 10.1109/ACCESS.2025.3590402.

[21] Q. Zhang, C. Wilson, K. Onesy, C. M. Miranda, and M. Y. Wang, “Autonomous Drone Navigation for Warehouse Inventory Using Fiducial Markers,” in 2024 Eighth IEEE International Conference on Robotic Computing (IRC), IEEE, Dec. 2024, pp. 278–283. doi: 10.1109/IRC63610.2024.11053981.

[22] M. Lam, J. Herrera, S. S. Afzal, K. Zhou, and F. Adib, “MiNav: Autonomous Drone Navigation Indoors Using Millimeter-Waves,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 9, no. 3, pp. 1–32, Sep. 2025, doi: 10.1145/3749464.

[23] L. S. de Assis, A. C. Daud Filho, L. G. S. da Rocha, K. C. T. Vivaldini, G. A. de P. Caurin, and M. M. Futai, “Methodology for tunnel inspection using drone with autonomous navigation,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 48, no. 3, p. 171, Mar. 2026, doi: 10.1007/s40430-025-06168-7.

[24] P. V. Hegde, M. R. Ahmed, and A. H. Nalband, “The Importance of Data Annotation for Autonomous Drone Navigation,” in 2024 Third International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON), IEEE, Nov. 2024, pp. 117–122. doi: 10.1109/TEECCON64024.2024.10939119.

[25] S. Li, M. M. O. I. Ozo, C. De Wagter, and G. C. H. E. de Croon, “Autonomous drone race: A computationally efficient vision-based navigation and control strategy,” Rob. Auton. Syst., vol. 133, p. 103621, Nov. 2020, doi: 10.1016/j.robot.2020.103621.

[26] J. Wu, Y. Ye, and J. Du, “Autonomous Drones in Urban Navigation: Autoencoder Learning Fusion for Aerodynamics,” J. Constr. Eng. Manag., vol. 150, no. 7, Jul. 2024, doi: 10.1061/JCEMD4.COENG-14787.

[27] M. S. Amiri and R. Ramli, “Visual Navigation System for Autonomous Drone using Fiducial Marker Detection,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 9, 2022, doi: 10.14569/IJACSA.2022.0130981.

[28] G. Solaimalai, K. J. Prakash, S. K. S, A. Bhagyalakshmi, P. Siddharthan, and K. R. Senthil Kumar, “Deep Reinforcement Learning for Autonomous Drone Navigation in Cluttered Environments,” in 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, May 2024, pp. 1–5. doi: 10.1109/ACCAI61061.2024.10602151.

[29] Y. Zhang, J. Xiao, and M. Feroskhan, “Learning Cross-Modal Visuomotor Policies for Autonomous Drone Navigation,” IEEE Robot. Autom. Lett., vol. 10, no. 6, pp. 5425–5432, Jun. 2025, doi: 10.1109/LRA.2025.3559824.

[30] K. E. Yong et al., “Drone Navigation System for Autonomous Mosquito Sampling in Tree Canopies,” in 2023 IEEE 9th World Forum on Internet of Things (WF-IoT), IEEE, Oct. 2023, pp. 01–06. doi: 10.1109/WF-IoT58464.2023.10539459.

[31] H. Chen, Y. Tang, A. Tsourdos, and W. Guo, “Contextualized Autonomous Drone Navigation Using LLMs Deployed in Edge-Cloud Computing,” in 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS), IEEE, Mar. 2025, pp. 1373–1378. doi: 10.1109/ICMLAS64557.2025.10967934.

[32] H. X. Pham, H. I. Ugurlu, J. Le Fevre, D. Bardakci, and E. Kayacan, “Deep learning for vision-based navigation in autonomous drone racing,” in Deep Learning for Robot Perception and Cognition, Elsevier, 2022, pp. 371–406. doi: 10.1016/B978-0-32-385787-1.00020-8.

[33] M. Navardi, A. Shiri, E. Humes, N. R. Waytowich, and T. Mohsenin, “An Optimization Framework for Efficient Vision-Based Autonomous Drone Navigation,” in 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), IEEE, Jun. 2022, pp. 304–307. doi: 10.1109/AICAS54282.2022.9869975.

[34] L. Zeng, H. Chen, D. Feng, X. Zhang, and X. Chen, “A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones,” IEEE/ACM Transactions on Networking, vol. 32, no. 1, pp. 713–728, Feb. 2024, doi: 10.1109/TNET.2023.3297876.

[35] R. H. Kumar, A. M. Vanjare, and S. N. Omkar, “Autonomous Drone Navigation using Monocular Camera and Light Weight Embedded System,” in 2023 International Conference for Advancement in Technology (ICONAT), IEEE, Jan. 2023, pp. 1–6. doi: 10.1109/ICONAT57137.2023.10080483.

[36] P. Chen, T. Ouyang, K. Luo, W. Hong, and X. Chen, “CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models,” IEEE Internet Things J., vol. 13, no. 4, pp. 5593–5609, Feb. 2026, doi: 10.1109/JIOT.2025.3648721.

[37] S. A. Khalil, S. Abdul-Rahman, S. Mutalib, and N. M. A. A. Dazlee, “Object Detection for Autonomous Vehicles with Sensor-based Technology Using YOLO,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1, pp. 129–134, Mar. 2022, doi: 10.18201/ijisae.2022.276.

[38] L. Hu et al., “Non-Reciprocal Interactions Based Emergent Navigation for 3D Autonomous Drones Swarm,” in 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Oct. 2025, pp. 441–446. doi: 10.1109/SMC58881.2025.11343351.

[39] A. Basiri, V. Mariani, and L. Glielmo, “Enhanced V-SLAM combining SVO and ORB-SLAM2, with reduced computational complexity, to improve autonomous indoor mini-drone navigation under varying conditions,” in IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Oct. 2022, pp. 1–7. doi: 10.1109/IECON49645.2022.9968605.

[40] J. Zhao, Y. Wang, Z. Cai, N. Liu, K. Wu, and Y. Wang, “Learning Visual Representation for Autonomous Drone Navigation via a Contrastive World Model,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, pp. 1263–1276, Mar. 2024, doi: 10.1109/TAI.2023.3283488.

[41] G. B. L, B. V, and A. M, “Development of Relevance Propagation Rule-based Systems and Faster Mask R-CNN for IoT-Enabled Surveillance Drones to Enhance Autonomous Navigation and Collision Avoidance,” International Journal of Electronics and Communication Engineering, vol. 12, no. 5, pp. 33–49, May 2025, doi: 10.14445/23488549/IJECE-V12I5P104.

[42] H. Gutierrez, J. Cornejo, and I. Bertaska, “uVGS-2: The Micro Video Guidance Sensor: A 6-DoF Robust Pose Estimator for Autonomous Proximity Maneuvers in Drones, Spacecraft and Mobile Robot Navigation,” Drones, vol. 10, no. 7, p. 535, Jul. 2026, doi: 10.3390/drones10070535.

[43] S. Suzuki, “Vision/INS/Altimeter-Based Navigation and Control for Autonomous Drones in Indoor Environments,” Journal of Robotics and Mechatronics, vol. 37, no. 4, pp. 815–824, Aug. 2025, doi: 10.20965/jrm.2025.p0815.

[44] A. A. Gogolev and P. G. Ermakov, “Algorithm for Integrating Data from the Autonomous Inertial Navigation Systems of Drones,” Russian Engineering Research, vol. 42, no. 9, pp. 936–938, Sep. 2022, doi: 10.3103/S1068798X22090106.

[45] L. J. Sayed, P. K. Souripalli, and S. S. Chiddarwar, “Autonomous Navigation of Drones Using Explainable Deep Reinforcement Learning in Foggy Environment,” in 2024 IEEE Students Conference on Engineering and Systems (SCES), IEEE, Jun. 2024, pp. 1–6. doi: 10.1109/SCES61914.2024.10652534.

[46] A. Geetha et al., “Autonomous navigation of tethered drone strings in cluttered environments through real-time obstacle detection and geometric tether control,” Results in Engineering, vol. 32, p. 112159, Dec. 2026, doi: 10.1016/j.rineng.2026.112159.

[47] W. Power, M. Pavlovski, D. Saranovic, I. Stojkovic, and Z. Obradovic, “Autonomous Navigation for Drone Swarms in GPS-Denied Environments Using Structured Learning,” in IFIP Advances in Information and Communication Technology 584, 2020, pp. 219–231. doi: 10.1007/978-3-030-49186-4_19.

[48] D. Kundra, J. Singh, S. R. Anwar, R. R, S. Sahoo, and J. R. M., “Drone Autonomous Navigation for Response to Disaster Using the Techniques of Swarm Intelligence,” in 2025 International Conference on Computational Intelligence, Security, and Artificial Intelligence (IntelliSecAI), IEEE, Dec. 2025, pp. 1–7. doi: 10.1109/IntelliSecAI66368.2025.11473218.

[49] X. Zhang, L. Zhang, H. Pei, and F. L. Lewis, “Part-based multi-task deep network for autonomous indoor drone navigation,” Transactions of the Institute of Measurement and Control, vol. 42, no. 16, pp. 3243–3253, Dec. 2020, doi: 10.1177/0142331220947507.

[50] Y. Pang, N. Song, and Q. Cai, “A Novel Method for Gyro Bias Estimation of On-Board Inertial Navigation System Prior to Launch of Autonomous Drone,” IEEE Trans. Industr. Inform., vol. 20, no. 4, pp. 5459–5468, Apr. 2024, doi: 10.1109/TII.2023.3333922.

[51] M. Gnanasekera, J. Katupitiya, A. V. Savkin, and A. H. T. E. De Silva, “A Range-Based Algorithm for Autonomous Navigation of an Aerial Drone to Approach and Follow a Herd of Cattle,” Sensors, vol. 21, no. 21, p. 7218, Oct. 2021, doi: 10.3390/s21217218.

[52] X. Li, H. Huang, and A. V. Savkin, “Autonomous Navigation of an Aerial Drone to Observe a Group of Wild Animals With Reduced Visual Disturbance,” IEEE Syst. J., vol. 16, no. 2, pp. 3339–3348, Jun. 2022, doi: 10.1109/JSYST.2021.3135982.

[53] K. F. Andersen, H. X. Pham, H. I. Ugurlu, and E. Kayacan, “Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network,” in 2022 European Control Conference (ECC), IEEE, Jul. 2022, pp. 1342–1348. doi: 10.23919/ECC55457.2022.9838538.

[54] Z. Lian, P. Shi, C.-C. Lim, and X. Yuan, “Fuzzy-Model-Based Lateral Control for Networked Autonomous Vehicle Systems Under Hybrid Cyber-Attacks,” IEEE Trans. Cybern., vol. 53, no. 4, pp. 2600–2609, Apr. 2023, doi: 10.1109/TCYB.2022.3151880.

[55] I. Jubran, F. Fares, Y. Alfassi, F. Ayoub, and D. Feldman, “Newton-PnP: Real-time Visual Navigation for Autonomous Toy-Drones,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Oct. 2022, pp. 13363–13370. doi: 10.1109/IROS47612.2022.9981922.

[56] T. Kavitha, K. R. Chaganti, S. L. R. Elicherla, M. R. Kumar, D. Chaithanya, and K. Manikanta, “Deep Reinforcement Learning for Energy Efficiency Optimization using Autonomous Waste Management in Smart Cities,” in 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), IEEE, Apr. 2025, pp. 272–278. doi: 10.1109/ICTMIM65579.2025.10988394.

[57] S. Qamar, S. H. Khan, M. A. Arshad, M. Qamar, J. Gwak, and A. Khan, “Autonomous Drone Swarm Navigation and Multitarget Tracking With Island Policy-Based Optimization Framework,” IEEE Access, vol. 10, pp. 91073–91091, 2022, doi: 10.1109/ACCESS.2022.3202208.

[58] S. Qamar, S. H. Khan, M. A. Arshad, M. Qamar, J. Gwak, and A. Khan, “Autonomous Drone Swarm Navigation and Multitarget Tracking With Island Policy-Based Optimization Framework,” IEEE Access, vol. 10, pp. 91073–91091, 2022, doi: 10.1109/ACCESS.2022.3202208.

[59] C. Zhang et al., “A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation,” Drones, vol. 7, no. 1, p. 34, Jan. 2023, doi: 10.3390/drones7010034.

[60] H. SOUILEM, C. BEN SALAH, M. TLIJA, and A. RABHI, “Advanced Autonomous Drone Navigation with Real-Time Dynamic Replanning and Multi-Agent Coordination,” Studies in Informatics and Control, vol. 34, no. 3, pp. 29–38, Sep. 2025, doi: 10.24846/v34i3y202503.

[61] J. Tanwar, S. K. Sharma, and M. Mittal, “Designing obstacle’s map of an unknown place using autonomous drone navigation and web services,” International Journal of Pervasive Computing and Communications, vol. 19, no. 1, pp. 154–169, Jan. 2023, doi: 10.1108/IJPCC-07-2020-0085.

[62] T. Alotaibi, K. Jambi, M. Khemakhem, F. Eassa, and F. Bourennani, “Deep Learning-Based Autonomous Navigation of 5G Drones in Unknown and Dynamic Environments,” Drones, vol. 9, no. 4, p. 249, Mar. 2025, doi: 10.3390/drones9040249.

[63] F. F. Nyboe, N. H. Malle, V. D. Hoang, and E. Ebeid, “Open-Source Hardware/Software Architecture for Autonomous Powerline-Aware Drone Navigation and Recharging,” in 2023 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, Jun. 2023, pp. 1233–1240. doi: 10.1109/ICUAS57906.2023.10155937.

[64] G. Gao, J. Lu, and W. Guan, “CE-Bi-RRT*: Enhanced Bidirectional RRT* with Cooperative Expansion Strategy for Autonomous Drone Navigation,” Drones, vol. 9, no. 12, p. 831, Nov. 2025, doi: 10.3390/drones9120831.

[65] M. Sartori, C. Singhal, N. Roy, D. Brunelli, and J. Gross, “AI and Vision Based Autonomous Navigation of Nano-Drones in Partially-Known Environments,” in 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), IEEE, Jun. 2025, pp. 307–314. doi: 10.1109/DCOSS-IoT65416.2025.00058.

[66] T. G. Silva et al., “Simulation-Based Autonomous Drone Navigation for Security and Monitoring Using Deep Reinforcement Learning,” in 2025 Brazilian Conference on Robotics (CROS), IEEE, Apr. 2025, pp. 1–6. doi: 10.1109/CROS66186.2025.11066101.

[67] T. Czarnecki, M. Stawowy, and A. Kadłubowski, “Cost-Effective Autonomous Drone Navigation Using Reinforcement Learning: Simulation and Real-World Validation,” Applied Sciences, vol. 15, no. 1, p. 179, Dec. 2024, doi: 10.3390/app15010179.

[68] H. Mohan, R. Dinesh, A. S Kumar, A. Mathai, and D. S, “Enhanced YOLOv8- Driven Traffic Rule Violation Detection with ROS-Integrated Autonomous Drone Navigation,” in 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS), IEEE, May 2024, pp. 1–6. doi: 10.1109/RAICS61201.2024.10689890.

[69] J. Wu, Y. Ye, and J. Du, “Multi-objective reinforcement learning for autonomous drone navigation in urban areas with wind zones,” Autom. Constr., vol. 158, p. 105253, Feb. 2024, doi: 10.1016/j.autcon.2023.105253.

[70] C.-Y. Lee, A. Khanum, N.-C. Wang, B. S. K. Karuparthi, and C.-S. Yang, “An Efficient Lane Following Navigation Strategy With Fusion Attention for Autonomous Drones in Urban Areas,” IEEE Trans. Veh. Technol., vol. 73, no. 3, pp. 3094–3105, Mar. 2024, doi: 10.1109/TVT.2023.3322808.