Speed Planning Study Of Driverless Mining Trucks In Open-pit Mines Based On High-definition Maps

The open pit mine road space is complex,and there is a risk of collision in the driving of the mining truck due to conflicting driving trajectories and restricted perception.The existing sensor-based perception obstacle avoidance method in the open pit mine scene not only has reliability problems caused by environmental interference,but also speed decisions such as emergency braking and emergency stopping and braking stopping and restarting,which leads to serious tire wear and fuel waste of the mine card and affects the passage efficiency of the mine card.In order to solve the above problems,this paper combines the special characteristics of the open-pit mining passage scenario and the advantages of driverless mining trucks that can accurately execute the planning speed,and proposes a full-speed planning method with the planning goal of passage efficiency and passage safety based on the all-weather over-the-horizon assistance function provided by the high precision map,which can reduce or even avoid braking before intersections while maintaining a safe distance from the vehicles in front and behind.The main research results are as follows:(1)A data structure of high-definition map in open-pit mine is designed to realize the construction of high-definition map,which provides data basis for the speed planning of unmanned mine truck in open-pit mine.(2)The classification of spatio-temporal conflicts in the operation of unmanned mine cards in open-pit mines is carried out,and the conflicts between mine cards in open-pit mines are classified in detail.The conflict detection based on road network topology and the conflict resolution algorithm based on time domain are proposed.(3)The trapezoidal speed planning method is improved,the adaptive trapezoidal speed curve model is constructed,and the speed planning algorithm for solving the full range conflict-free speed curve is proposed.(4)The algorithm and scenarios proposed in this paper are implemented based on Python,and a simulation platform is established to design a variety of scenarios for implementation,and the results verify the effectiveness and adaptability of the algorithm in this paper.