An Improved A-Star Algorithm for Smooth Paths Using Regional Cost and Corner Penalty

In the domain of path planning, this paper introduces an advanced algorithm that integrates regional cost optimization and corner penalty to enhance traffic efficiency and path smoothness. Building upon the A star algorithm, this method incorporates environmental characteristics through the introduction of a regional cost function and a dynamic weight adjustment mechanism. These enhancements allow for precise adaptation to varying terrain types, such as cement roads and sand. Additionally, an improved corner penalty mechanism effectively minimizes sharp turns, thereby enhancing path smoothness. To address potential issues with obstacle crossing during path smoothing, a Ramer-Douglas-Peucker based recursive rath simplification algorithm is employed to ensure physical feasibility while maintaining path smoothness. Experimental results demonstrate that the proposed algorithm achieves superior performance in terms of path length, smoothness, and passability across diverse environmental scenarios, particularly in complex obstacle distributions. This significantly improves the practicality of path planning, providing robust theoretical support and practical references for applications such as autonomous driving and robot navigation.