As robotics technology continues to mature, it has made people’s lives more convenient. However, robot path planning and obstacle avoidance have become key research issues. To address these challenges, this study proposes a research framework for the construction and application of hybrid topological maps for robot path planning and obstacle avoidance in complex scenarios. Under the support of mobile robot SLAM theory, a topologygrid hybrid map is constructed. Since the topology-grid hybrid map exists in both static and dynamic scenarios, this study designs a static path planning and obstacle avoidance algorithm based on an improved A* algorithm and a dynamic planning and obstacle avoidance algorithm based on an improved TEB algorithm, and conducts validation instance analysis on both algorithms. In dynamic scenarios, the navigation success rate, average path length, and average time consumption of the proposed algorithms are superior to those of traditional algorithms, with values of 99.00%, 3.415m, and 16.35s, respectively. In static scenarios, similar phenomena are observed, demonstrating the effectiveness of the two dynamic path planning and obstacle avoidance algorithms. This research provides guiding value for the development of robot path planning and obstacle avoidance.
