REFERENCES

1. Zhou Y, Baras JS. Reachable set approach to collision avoidance for UAVs. In 2015 54th IEEE Conference on Decision and Control (CDC). 2015;pp. 5947-52.

2. Iacono M, Sgorbissa A. Path following and obstacle avoidance for an autonomous UAV using a depth camera. Robot Auton Syst 2018;106:38-46.

3. Radmanesh M, Kumar M, Guentert PH, Sarim M. Overview of path-planning and obstacle avoidance algorithms for UAVs: a comparative study. Unmanned Syst 2018;6:95-118.

4. Ait Saadi A, Soukane A, Meraihi Y, Benmessaoud Gabis A, Mirjalili S, Ramdane-Cherif A. UAV path planning using optimization approaches: a survey. Arch Comput Methods Eng 2022;29:4233-84.

5. Maini P, Sujit PB. Path planning for a UAV with kinematic constraints in the presence of polygonal obstacles. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS); 2016. pp. 62-7.

6. Mandloi D, Arya R, Verma AK. Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3D environment. Int J Syst Assur Eng Manag 2021;12:990-1000.

7. Wu X, Xu L, Zhen R, Wu X. Biased sampling potentially guided intelligent bidirectional RRT$$\ast$$ algorithm for UAV path planning in 3D environment. Math Probl Eng 2019;2019:1-12.

8. Liu H, Liu HH, Chi C, Zhai Y, Zhan X. Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight. Aerosp Syst 2020;3:229-41.

9. Perez-Carabaza S, Besada-Portas E, Lopez-Orozco JA, de la Cruz JM. Ant colony optimization for multi-UAV minimum time search in uncertain domains. Appl Soft Comput 2018;62:789-806.

10. Li J, Deng G, Luo C, Lin Q, Yan Q, Ming Z. A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems. IEEE Trans Veh Technol 2016;65:9585-96.

11. Shao S, Peng Y, He C, Du Y. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Trans 2020;97:415-30.

12. Roberge V, Tarbouchi M, Labonte G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Industr Inform 2013;9:132-41.

13. Jembre YZ, Nugroho YW, Khan MT, et al. Evaluation of reinforcement and deep learning algorithms in controlling unmanned aerial vehicles. Appl Sci 2021;11:7240.

14. Wu J, Sun Y, Li D, et al. An adaptive conversion speed Q-learning algorithm for search and rescue UAV path planning in unknown environments. IEEE Trans Veh Technol 2023:1-14.

15. Mnih V, Kavukcuoglu K, Silver D, et al. Playing Atari with deep reinforcement learning. arXiv 2013. Available from: https://doi.org/10.48550/arXiv.1312.5602[Last accessed on 16 Aug 2023].

16. Ye Z, Wang K, Chen Y, Jiang X, Song G. Multi-UAV navigation for partially observable communication coverage by graph reinforcement learning. IEEE Trans Mob Comput 2023;22:4056-69.

17. Wang L, Wang K, Pan C, Xu W, Aslam N, Nallanathan A. Deep reinforcement learning based dynamic trajectory control for UAV-assisted mobile edge computing. IEEE Trans Mob Comput 2022;21:3536-50.

18. Lin Z, Castano L, Mortimer E, Xu H. Fast 3D collision avoidance algorithm for Fixed Wing UAS. J Intell Robot Syst 2019;97:577-604.

19. Jiang L, Huang H, Ding Z. Path planning for intelligent robots based on deep Q-learning with experience replay and heuristic knowledge. IEEE/CAA J Automatica Sinica 2020;7:1179-89.

20. Schaul T, Quan J, Antonoglou I, Silver D. Prioritized experience replay. arXiv 2016. Available from: https://arxiv.org/abs/1511.05952[Last accessed on 16 Aug 2023].

21. Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning. Nature 2015;518:529-33.

22. She D, Jia M. Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate. Measurement 2019;135:368-75.