Global path planning on uneven terrain at construction sites for robots based on an improved A* Algorithm

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With the increasing application of construction robots on construction sites, autonomous path planning in unstructured and uneven construction sites has become an urgent challenge. Current path planning approaches face several issues, including prolonged computation times, low efficiency, and redundant nodes, often resulting in impractical paths for robots. Addressing these concerns, this study introduces a global path planning method based on an enhanced A* algorithm to ensure safe and robust navigation of construction robots in such challenging environments. The Improved A* algorithm initially incorporates a bidirectional alternating search strategy to expedite computational speed. Subsequently, it employs path node filtering to mitigate issues associated with bidirectional search and reduce the number of critical nodes, thereby enhancing search efficiency. Furthermore, the introduction of slope constraints decreases the robot's climbing and tilting angles, augmenting the safety of the planned paths. Finally, the paths are smoothed using Bézier curve fitting, facilitating better motion control for the robots. The efficacy of the improved algorithm was validated through experiments on elevation maps with varying terrain and obstacle densities. Simulation results indicate that, compared to the traditional A* algorithm, the Improved A* algorithm reduced computation time by 71.05% to 82.90% and the number of critical nodes by 51.94% to 70.53%, while only increasing the path length by 14.6% to 37.84%. Additionally, there was a significant reduction in climbing and tilting angles, and the paths have become smoother. Therefore, this method not only improves efficiency while generating safe and reliable paths in unstructured and uneven construction sites, but also enables robots to adapt to complex environments, and promotes automation and intelligent construction processes.