Abstract
During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.
Highlights
Cost control has always been the top priority in the mining industry, and how to control the cost is one of the critical points for mining companies to make profits
In order to reduce the extra cost, this paper proposes a high-precision planning map construction method based on digital surface model (DSM) and an improved Hybrid A* algorithm
The obstacle binary map is constructed by the elevation scanning method, and the obstacle cost map is made from generalized Voronoi diagram (GVD)
Summary
Cost control has always been the top priority in the mining industry, and how to control the cost is one of the critical points for mining companies to make profits. There are a lot of open spaces in the cutting zone of open-pit mines without any obstacles In this scenario, the path planner considers the road surface as a passable area without obstacles, planning a path directly to the loading position which may result in extra tire cost because of the ground roughness. In order to reduce the extra cost, this paper proposes a high-precision planning map construction method based on DSM and an improved Hybrid A* algorithm This planning map contains both surface obstacles and roughness information. The main contributions of this paper are as follows: We propose a truck path planning method based on high-precision digital map for the final transportation section of open-pit mines, which can effectively reduce the driving cost of trucks.
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