Research on Robot Path Planning Based on Improved A* Algorithm

Abstract

Robot path planning is a key core technology for realizing autonomous navigation of robots. Among them, the A* algorithm has been widely used due to its good balance between path optimality and search efficiency. However, the traditional A* algorithm faces problems such as excessive expansion nodes, numerous path inflection points, poor smoothness, and insufficient adaptability caused by fixed heuristic functions in complex environments, which limits its performance in scenarios with high real-time requirements. To solve the above problems, this study aims to systematically improve the traditional A* algorithm. To resolve redundant algorithmic operations and insufficient computational efficiency, we first put forward a bidirectional search strategy. By executing two parallel searches from the initial and target nodes, the proposed method drastically cuts the search space and lowers overall computational costs. Second, for better environmental adaptability of the algorithm, we construct a dynamically weighted heuristic function capable of real-time adjustment of heuristic weights with varying obstacle densities, which maintains an optimal dynamic balance between search efficiency and path quality. In addition, this study also introduces a path post-processing method to smooth and optimize the initially planned path, so as to eliminate unnecessary turning points and generate a smooth path that is more in line with the kinematic constraints of robots. To verify the effectiveness of the improved algorithm, comparative experiments are conducted in multiple typical simulation environments. The experimental results demonstrate that, compared with the traditional A* algorithm and other state-of-the-art improved algorithms, the method proposed in this study not only preserves the global optimality of the generated path, but also significantly reduces both the number of expansion nodes and the computational time required for path planning. Furthermore, it produces a smooth path characterized by shorter length, fewer turns and enhanced safety. This work confirms the feasibility and superiority of the proposed improvement strategy in improving the performance of the A* algorithm, provides an effective solution for solving the balance problem between efficiency and quality in robot path planning, and has positive reference value for promoting the practical application of robots in complex dynamic environments.