Research and Algorithm Optimization of Path Planning Based on A* Algorithm

In logistics delivery on the plateau and frigid border, traditional methods like human or animal power are inefficient. Vehicles like automobiles face high costs and difficulties. The plateau area poses significant challenges. However, unmanned vehicles show strong adaptability in harsh environments, enabling efficient logistics supply. This paper constructs an unmanned vehicle path selection model, optimizing it through the A* algorithm for better logistics transportation support. The A* algorithm is optimized considering stability, safety, and timeliness, with corresponding weights assigned. The mileage-time, elevation-time, and slope-mileage curves are plotted. Principles include prioritizing safety, then timeliness, and finally stability, and selecting the route with less time consumption when path lengths are equal. Technical indicators of unmanned vehicles are added, constructing a transportation network. The traditional breadth-first traversal model is replaced by the improved A* algorithm for efficiency. Movement direction is encoded as the vehicle’s heading at the next grid point, obtaining optimal path planning.