Path Planning of Unmanned Mine Truck Based on Improved Grey Wolf Algorithm
In view of the shortcomings of conventional transportation path planning methods in complex terrain environments of open-pit mines,such as being prone to local optima,slow path convergence speed,and long time consumption,an improved grey wolf algorithm was proposed for path planning of unmanned mine truck.Considering the characteristics of terrain slope fluctuations in mining areas,a slope-speed model was established to introduce the speed of mine truck in the grid environment of the mining area into the transition rules of uphill and downhill driving states.A convergence factor combining sine and cosine transform was constructed to better balance the global and local search ability.The optimal grey wolf individuals were cross perturbed to enhance the ability of the wolves to jump out of the local optimum.The simulation test results show that in a complex grid environment of 20 kmX20 km mining area,the improved grey wolf algorithm reduces the average path planning and average running time by 2.53％-4.77％and 3.97％-12.32％compared to genetic algorithm,cosine algorithm,and traditional grey wolf algorithm,respectively.In terms of optimization ability,convergence speed,and stability,it is superior to traditional algorithms.The research can provide a reference for the transportation operation of unmanned mine trucks in open-pit mines in the construction of intelligent mines.
Intelligent mineUnmanned mine truckOpen-pit minePath planningImproved grey wolf algorithm
万方数据
维普
