Comparison of machine learning algorithms for dynamic robot path planning

The purpose of research. The purpose of this scientific work is to conduct a comprehensive theoretical and analytical review of modern machine learning algorithms used to solve the problems of dynamic route planning for mobile robots. The main focus is on a comparative assessment of the effectiveness of various learning paradigms – reinforcement learning, teacher-based learning, and hybrid approaches – in a changing and uncertain environment where rapid adaptation, learnability, and algorithm stability are important.

Methods. The study is based on an analysis of more than 40 peer-reviewed scientific publications selected from leading international academic databases for the period from 2020 to 2024. A structured methodology was used, including descriptive, comparative, and analytical approaches. The main evaluation criteria were: convergence rate; computational efficiency; generalization ability; noise tolerance; adaptability to real-time and stable behavior in changing conditions.

Results. It is shown that tabular algorithms provide basic navigation functionality, but they do not scale for complex tasks. Deep models have a high degree of adaptability and efficiency. Teaching with a teacher demonstrates accuracy in the presence of expert data, but is vulnerable to the accumulation of errors. Hybrid architectures combining graph neural networks and symbolic modeling achieve the best interpretability and stability in an unstable environment.

Conclusion. The results obtained form a reliable theoretical basis for the selection and application of autonomous navigation algorithms. The comparative analysis highlights the value of flexible, scalable, and explicable models in intelligent robotics systems of a new generation.

1. Li X., Zikry A.H.A., Hassan A.Y., Shaban W.I., Abdel-Momen S.F. Dynamic path planning of mobile robots using adaptive dynamic programming. Expert Systems with Applications. 2024;235:121112. https://doi.org/10.1016/j.eswa.2023.121112

2. Hou J., Wang J., Li P. Dynamic path planning for mobile robots by integrating improved sparrow search algorithm and dynamic window approach. Actuators. 2024;13(1):24. https://doi.org/10.3390/act13010024

3. Yang L., Bi J., Yuan H. Dynamic path planning for mobile robots with deep reinforcement learning. IFAC-PapersOnLine. 2022;55(11):19–24. https://doi.org/10.1016/j.ifacol.2022.07.004

4. Hewawasam H.S., Ibrahim M.Y., Appuhamillage G.K. Past, present and future of pathplanning algorithms for mobile robot navigation in dynamic environments. IEEE Open Journal of the Industrial Electronics Society. 2022;3:353–365.

5. Marashian A., Razminia A. Mobile robot’s path-planning and path-tracking in static and dynamic environments: Dynamic programming approach. Robotics and Autonomous Systems. 2024;172:104592. https://doi.org/10.1016/j.robot.2023.104592

6. Liu L., Wang Y., Chen J. Path planning techniques for mobile robots: Review and prospect. Expert Systems with Applications. 2023;227:120254. https://doi.org/10.1016/j.eswa.2023.120254

7. Sánchez-Ibáñez J.R., Pérez-del-Pulgar C.J., García-Cerezo A. Path planning for autonomous mobile robots: A review. Sensors. 2021;21(23):7898. https://doi.org/10.3390/s21237898

8. Lu Y., Da C. Global and local path planning of robots combining ACO and dynamic window algorithm. Scientific Reports. 2025;15(1):9452.

9. Shi K., Zhang J., He M. Dynamic path planning of mobile robot based on improved simulated annealing algorithm. Journal of the Franklin Institute. 2023;360(6):4378–4398. https://doi.org/10.1016/j.jfranklin.2023.02.012

10. Wu Q., Liu F., Zhao Y. Real-time dynamic path planning of mobile robots: A novel hybrid heuristic optimization algorithm. Sensors. 2019;20(1):188. https://doi.org/10.3390/s20010188

11. Zhu K., Zhang T. Deep reinforcement learning based mobile robot navigation: A review. Tsinghua Science and Technology. 2021;26(5):674–691.

12. Mannan A., Rahman S., Akhtaruzzaman M. Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review. International Journal of Communication Systems. 2023;36(5):e5423.

13. Tang Y., Ma J., Zhang Z. Perception and navigation in autonomous systems in the era of learning: A survey. IEEE Transactions on Neural Networks and Learning Systems. 2022;34(12):9604–9624.

14. Kiran B.R., Sobh I., Talpaert V., Mannion P., Al Sallab A., Yogamani S., Pérez P. Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems. 2021;23(6):4909–4926.

15. Tai L., Paolo G., Liu M. Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2017. P. 31–36.

16. Rudenko A., Palmieri L., Arras K.O. Predictive planning for a mobile robot in human environments. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Workshop on AI Planning and Robotics. 2017.

17. Han D., Gu J., Cheng X. A survey on deep reinforcement learning algorithms for robotic manipulation. Sensors. 2023;23(7):3762.

18. Hachour O. Path planning of autonomous mobile robot. International Journal of Systems Applications, Engineering & Development. 2008;2(4):178–190.

19. Kaluđer H., Brezak M., Petrović I. A visibility graph based method for path planning in dynamic environments. In: 2011 Proceedings of the 34th International Convention MIPRO. IEEE; 2011. P. 717–721.

20. Mnih V., Kavukcuoglu K., Silver D., Rusu A.A., Veness J., Bellemare M.G., et al. Human-level control through deep reinforcement learning. Nature. 2015;518(7540):529–533.

21. Carta S., Corriga G., Recupero D.R., Saia R., Satta R. Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting. Expert Systems with Applications. 2021;164:113820.

22. Gu Y., He H., Ni Y. Proximal policy optimization with policy feedback. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021;52(7):4600–4610.

23. Xiao Y., Tan W., Amato C. Asynchronous actor-critic for multi-agent reinforcement learning. Advances in Neural Information Processing Systems. 2022;35:4385–4400.

24. Li H., He H. Multiagent trust region policy optimization. IEEE Transactions on Neural Networks and Learning Systems. 2023;35(9):12873–128878.

25. Sigaud O. Combining evolution and deep reinforcement learning for policy search: A survey. ACM Transactions on Evolutionary Learning. 2023;3(3):1–20.

26. Li S.E. Deep reinforcement learning. In: Reinforcement Learning for Sequential Decision and Optimal Control. Singapore: Springer Nature Singapore; 2023. P. 365–402.

27. Eraqi H.M., Moustafa M.N., Honer J. Dynamic conditional imitation learning for autonomous driving. IEEE Transactions on Intelligent Transportation Systems. 2022;23(12):22988–23001.

28. Duan A., Liu B., Zhang J. A structured prediction approach for robot imitation learning. The International Journal of Robotics Research. 2024;43(2):113–133.

29. Le Mero L., Singh R., Tran L., El Asri L., Zhang Y. A survey on imitation learning techniques for end-to-end autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems. 2022;23(9):14128–14147.

30. Fang Q., Li S., Wang X., Li W. Target‐driven visual navigation in indoor scenes using reinforcement learning and imitation learning. CAAI Transactions on Intelligence Technology. 2022;7(2):167–176.

31. Lee Z.E., Zhang K.M. Generalized reinforcement learning for building control using Behavioral Cloning. Applied Energy. 2021;304:117602.

32. Zeng A., Song S., Lee J., Rodriguez A., Funkhouser T. Transporter networks: Rearranging the visual world for robotic manipulation. In: Conference on Robot Learning. PMLR; 2021. P. 726–747. URL: https://arxiv.org/abs/2010.14406 (accessed 17.03.2025).

33. Pan W., Lv B., Peng L. Research on AUV navigation state prediction method using multihead attention mechanism in a CNN-BiLSTM model. In: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024). Vol. 13229. SPIE; 2024. P. 831–840.

34. Molina-Leal A., Martinez-Rodrigo A., Cuenca-Asensi S., Campoy P. Trajectory planning for a mobile robot in a dynamic environment using an LSTM neural network. Applied Sciences. 2021;11(22):10689.

35. Zhang W., Li Y., Li H., Yu J. Dyngmp: Graph neural network-based motion planning in unpredictable dynamic environments. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2023. P. 858–865.

36. Wei W., Sun Y., Zhang Y., Wang H., Li S. Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 2023;24(8):8393–8398.

37. Xie Z., Dames P. Stochastic occupancy grid map prediction in dynamic scenes. In: Conference on Robot Learning. PMLR; 2023. P. 1686–1705.

38. Zadem M. Automatic Symbolic Goal Abstraction via Reachability Analysis in Hierarchical Reinforcement Learning. Institut Polytechnique de Paris; 2024.

39. Alotaibi A., Ahmad M., Rahman A., Alrashidi A. Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study. Technologies. 2024;12(9):157.