Abstract:

In recent years， path planning methods of autonomous driving mining truck that consider safety and efficiency have been gradually maturing and have been implemented in various mining scenarios. Simultaneously， the utilization of path planning methods to improve the fuel efficiency of mining trucks is paid more and more attention to by both the industry and academia. In response to this requirement， a method for energy-efficient path planning of autonomous driving trucks within mining environments is proposed in this paper. Its primary features encompass the utilization of composite dynamic sampling for S-L （Station - Lateral deviation） and S-T （Station - Time）， based on speed， road gradient， and obstacles. A fuel consumption index for typical terrain scenarios in mining environments is established. Additionally， a comprehensive path evaluation model of safety， efficiency and energy consumption is introduced. To prevent the entrapment of the evaluation model's weights in local optima， an adaptive optimization method based on the particle swarm algorithm with simulated annealing strategy is designed. Through testing in real mining scenarios， the method proposed in this paper has exhibited an average improvement of 11.28% in fuel economy metrics compared to existing methods.

Key words: intelligent vehicle, autonomous driving, fuel economy, local path planning