The path planning of a mobile robot based on the improved A-star algorithm combined with DWA.*

In recent years, the significance of path planning has been increasingly recognized as a fundamental component in enabling autonomous robot navigation. This critical technology has emerged as an essential element for mobile robots to achieve self-directed movement and obstacle avoidance in complex environments. Currently, the $A^{*}$ search methodology and Dynamic Window Approach (DWA) are prominent techniques. A-algorithm is able to compute optimal global paths in static environments. However, in dynamic environments, its performance is limited, especially in real-time obstacle avoidance [1] and trajectory tracking. Comparatively, DWA focuses on local path planning and can dynamically adjust the robot speed and direction to avoid obstacles in real time. But it relies on global path planning and lacks global optimality. This study presents an innovative navigation strategy that integrates the A* search mechanism with the Dynamic Window Approach. The proposed methodology synthesizes global path optimization with local obstacle avoidance, addressing the inherent constraints of each standalone algorithm. The experiments results show that the postoptimization algorithm generates smooth paths and achieved the $19.13 \%$ reduction in search time.