TriLayer-Nav: A Tri-Layer Navigation Framework Integrating A*, Dynamic Window Approach, and Model Predictive Control for Differential-Drive Robots

This paper introduces TriLayer-Nav, a modular tri-layer hybrid navigational system that integrates global planning, reactive local planning, and predictive control optimization to achieve smooth, energy-efficient, and robust navigation of differential-drive ground robots. The architecture employs the A* algorithm for the computation of a collisionfree global path, the Dynamic Window Approach for reactive obstacle avoidance, and Model Predictive Control for refining the Dynamic Window Approach commands while at the same time satisfying non-holonomic and actuator constraints. The different layers function in cascade (A* at a low frequency, Dynamic Window Approach in real-time, and Model Predictive Control continuously refining commands), with final wheel-level tracking being performed by a PID controller. TriLayer-Nav has undergone intensive simulation and validation in a physics-based environment exploiting the MuJoCo platform, allowing detailed modelling of rigid-body dynamics, frictional interactions, and actuator feedback. In the simulation protocol, a complete hierarchical allocation of commands was highlighted throughout the system and at the same time maintained realtime computation throughput. The findings show that TriLayer-Nav produces smoother paths with less curvature discontinuities, reduced control oscillations, better heading accuracy, and lower energy use, and with a success rate of 96.6%, while path efficiency is better than that of implementations that solely rely on A* or Dynamic Window Approach. Layer interaction provides better reaction to environmental changes and sensor noise. It is also computationally viable to run the framework in real-time by using a nonlinear Model Predictive Control solver and its modular hierarchy allows it to be implemented in structured and semi-structured environments on a large scale. To sum up, the findings deliver strong evidence that combining global planning, local reactive planner and predictive optimization is a significant improvement in enhancing the reliability, stability and energy efficiency of autonomous navigation. TriLayer-Nav is a generalizable and computationally efficient navigation solution that can be applicable to a wide range of ground robotic systems used in dynamic and uncertain environments.

1. Rodríguez-Molina, A., Herroz-Herrera, A., Aldape-Pérez, M., et al., Dynamic path planning for the differential drive mobile robot based on online metaheuristic optimization, Mathematics, 2022, vol. 10, no. 21, p. 3990, doi: 10.3390/math10213990.

2. Chen, J. and Cheng, J., Study of robot path planning with improved A* and DWA algorithm fusion, Academic Journal of Science and Technology, 2024, vol. 9, no. 2, pp. 77–82, doi: 10.54097/vkb7a649

3. Chen, X., Yang, C., Hu, H., Gao, Y., Zhu, Q., and Shao, G., A hybrid DWA-MPC framework for coordinated path planning and collision avoidance in articulated steering vehicles, Machines, 2024, vol. 12, no. 12, p. 939. https://doi.org/10.3390/machines12120939.

4. Li, K., Zhang, D., Li, X., and Su, Y., A study of mobile robot path planning by fusing improved A* and DWA algorithms, Proc. China Automation Congress (CAC). Chongqing, 2023, pp. 2242–2247, doi: 10.1109/CAC59555.2023.10450791.

5. Yang, H. and Teng, X., Mobile robot path planning based on enhanced dynamic window approach and improved A* algorithm, Journal of Robotics, 2022, vol. 2, pp. 1–9, doi: 10.1155/2022/2183229.

6. Jiang, Z., Wang, W., Sun, W., and Da, L., Path planning method for mobile robot based on a hybrid algorithm, Journal of Intelligent & Robotic Systems, 2023, vol. 109, no. 3, pp. 1–16, doi: 10.1007/s10846-023-01985-1.

7. Todorov, E., Erez, T., and Tassa, Y., MuJoCo: A physics engine for model-based control, Proc. IEEE/ RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 2012, pp. 5026–5033, doi: 10.1109/IROS.2012.6386109.

8. Wang, Z., Wang, S., Xie, Y., Xiong, T., and Wang, C., Autonomous navigation of mobile robots: A hierarchical planning-control framework with integrated DWA and MPC, Sensors, 2025, vol. 25, no. 7, p. 2014, doi: 10.3390/s25072014.

9. Šelek, A., Seder, M., and Petrović, I., Smooth autonomous patrolling for a differential-drive mobile robot in dynamic environments, Sensors, 2023, vol. 23, no. 17, p. 7421, doi: 10.3390/s23177421

10. Vulcano, V., Tarantos, S., Ferrari, P., and Oriolo, G., Safe robot navigation in a crowd combining NMPC and crowd prediction, Proc. Conference on Decision and Control (CDC), 2022, pp. 3321–3328, doi: 10.1109/CDC51059.2022.9993397.

11. Lim, J., Lee, H., and Choi, J., Nonlinear model predictive control with cost function scheduling for a wheeled mobile robot, IEEE International Conference on Intelligent Robots and Systems, 2022, p. 5664–5670, doi: 10.1109/IROS47612.2022.9981066.

12. Lu, Z., Feng, K., Xu, J., Chen, H., and Lou, Y., Robot safe planning in dynamic environments based on model predictive control using control barrier function, Proc. 2024 China Automation Congress (CAC), 2024, doi: 10.1109/CAC63892.2024.10864636.

13. Guo, R., Ren, X., and Bao, C., Path planning for mobile robots based on a hybrid A* – DWA approach, Electronics, 2025, vol. 14, no. 16, pp. 3221, https://doi.org/10.3390/electronics14163221.

14. He, Q., Wang, Z., Li, K., Zhang, Y., and Li, M., Research on autonomous navigation of mobile robots based on IA-DWA algorithm, Scientific Reports, 2025, vol. 15, p. 2099, doi: 10.1038/s41598-024-84858-3.

15. Liu, H., Feng, Y., Dong, W., Fan, K., Wang, C., and Gao, Y., Hierarchical learning-enhanced MPC for safe crowd navigation with heterogeneous constraints, arXiv, preprint, arXiv:2506.09859, 2025.

16. Nguyen, C.T., Duc, M.D., and Hoang, V.X., Development of an indoor localization and navigation system based on monocular SLAM for mobile robots, arXiv, preprint, arXiv:2411.05337, 2024, doi: 10.48550/arXiv.2411.05337.

17. Tang, Y., Path planning trends for autonomous mobile robot navigation, Sensors, 2025, vol. 25, no. 4, p. 1206, doi: 10.3390/s25041206.

18. Yang, L., Li, P., Qian, S., Quan, H., Miao, J., Liu, M., Hu, Y., and Memetimin, E., Path planning technique for mobile robots: A review, Machines, 2023, vol. 11, p. 980, https://doi.org/10.3390/machines11100980.

19. Hou, J., Jiang, W., Luo, Z., Yang, L., Hu, X., and Guo, B., Dynamic path planning for mobile robots by integrating improved Sparrow search algorithm and dynamic window approach, Actuators, 2024, vol. 13, p. 24, https://doi.org/10.3390/act13010024.