Cluster Path Planning Method in Mountainous Areas Based on Optimal Timeliness

This study addresses the path planning problem of road roller unmanned aerial vehicle swarms in mountainous terrains, aiming to improve compaction and optimize operational efficiency. Global scheduling is based on environmental information and task planning, while local path planning adjusts paths using real-time sensor data to ensure safety and efficiency without requiring global optimality. A dynamic spatial optimal path method is proposed, considering construction quality, swarm efficiency, and path safety. A dynamic spatial grid map is used to represent the roller’s surface information, and collision prediction is based on obstacle occupancy and temporal trends. A model for key mountainous highway scenarios is established to predict collisions between unmanned rollers and surrounding obstacles. The study also investigates the generation and optimization of dynamic grid maps for roadwork conditions. Experimental results show significant improvements in the following areas: 1) elimination of sharp turns in the trajectory, 2) path tracking accuracy of up to 5 cm, and 3) improved compaction uniformity with density distribution concentrated between 98.8%-99.8%, and a 47% reduction in variability. These results demonstrate the effectiveness of the proposed dynamic path planning method in complex mountainous environments.