Research on collaborative positioning of electric wheel trucks in open-pit mining areas based on hybrid metaheuristic algorithm

When the robust state of single vehicles of electric wheel trucks in open-pit mining areas suddenly changes, it leads to a significant increase in the impact of geometric configuration degradation and non-line of sight errors on auxiliary reference nodes, resulting in high errors in collaborative positioning between vehicles. Therefore, this paper proposes a research on collaborative positioning of electric wheel trucks in open-pit mining areas based on hybrid metaheuristic algorithm. Estimate the robust state of single vehicles of electric wheel trucks in open-pit mining areas, design multiple fading factor matrices, and achieve differentiated tracking of multidimensional states such as position, speed, and heading. Establish a collaborative positioning network with the robust state of single vehicles as auxiliary reference nodes, and alleviate the effects of geometric configuration degradation and non-line of sight errors through reputation weighted solution and displacement threshold screening. Dynamically adjusting the execution probability of the hybrid metaheuristic algorithm and forming an adaptive closed-loop through feedback mechanism, it has the ability to adapt to complex environments such as bumpy roads and signal obstruction in mining areas, and locate electric wheel trucks in open-pit mining areas. The experimental results show that the estimated trajectory is highly consistent with the real trajectory, the curve is smooth and has no abnormal spikes, the positioning coordinates are highly consistent with the real position, and the maximum positioning error is only 0.3 m. The performance improvement rate μ value is as high as 96.4%, and it has high positioning accuracy in complex mining environments.