Path Planning of Mobile Robot Using Improved A* Algorithm in Static Environments

One of the main challenges in autonomous mobile robot navigation is path planning, requiring effective, collision-free paths in static environments. Although traditional algorithms such as A* are extensively utilized, they can be computationally expensive and frequently produce inefficient pathways with unnecessary turns and sharp angles, especially in more complex cases. This work presents an improved A* algorithm that optimizes path smoothness and efficiency by considering an angle-based cost factor with the conventional A* algorithm and post-processing techniques. The algorithm is tested in the Robot Operating System (ROS), and Rviz is utilized for path visualization and computation. Numerous simulations show that the proposed approach consistently outperformed the conventional A* algorithm. The path length is reduced by 5.84% — 7.27% in various scenarios from obstacle-free to multi-obstacle cases, respectively. The proposed approach also reduces the execution time by 1.56% in obstacle-free cases while maintaining almost equal execution speeds in more complicated cases. The impact of the angle-based cost factor on path efficiency and smoothness has been investigated in detail; higher values improved path smoothness and lessened sharp turns. Thus, the proposed approach enhances path quality while maintaining computational efficiency, resulting in a more reliable solution for autonomous robot navigation in static environments.