Autonomous navigation and visual navigation in robot mission execution

Abstract

Navigating autonomously in complex environments remains a significant challenge, as traditional methods relying on precise metric maps and conventional path planning algorithms often struggle with dynamic obstacles and demand high computational resources. To address these limitations, we propose a topological path planning approach that employs Bernstein polynomial parameterization and real-time object guidance to iteratively refine the preliminary path, ensuring smoothness and dynamic feasibility. Simulation results demonstrate that our method outperforms MSMRL, ANS, and NTS in both weighted inverse path length and navigation success rate. In real-world scenarios, it consistently achieves higher success rates and path efficiency compared to the widely used OGMADWA method. These findings confirm that our approach enables efficient and reliable navigation in dynamic environments while maintaining strong adaptability and robustness in path planning.