Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
Youngmi Baek, Jung Kyu Park · Applied Sciences · 2025
In this paper, we address the challenge of memory consumption in mobile robot navigation, particularly when using grid-based maps for accurate environment representation. While grid-based maps are widely used, their large memory requirements make them unsuitable for embedded systems. To overcome this limitation, we propose a novel path planning algorithm that rapidly generates secure paths using a hybrid map. This hybrid map significantly reduces memory usage compared to grid-based maps while maintaining the efficiency of a topological map. Experimental results demonstrate that the proposed method requires only 1.5% of the computation time needed for grid-based path planning, ensuring faster and more efficient navigation. Furthermore, the approach provides robust and secure paths, making it ideal for mobile robot applications. This method holds promise for advancing autonomous navigation in resource-constrained environments.