| The large-scale electric shovel is one kind of large mechanical excavators and usually used in the open-pit mining to excavate the blasted material,and it is of great significance to the country ’s resource mining and energy security.The excavation of the existing large-scale electric shovel is completely manually operated.Due to the huge size of the shovel,the operator ’s long viewing distance,and the complex and variable mine environment,the problems mainly exist such as high energy consumption,low efficiency,large vibration and discontinuous excavation process.In order to solve the above problems caused by manual operation,realize the autonomous operation of unmanned operation of the electric shovel,and ensure its "high efficiency,energy saving,and safely",the study of trajectory planning and intelligent excavation system of large-scale electric shovel is the key link to realize the independence of electric shovel.Therefore,this paper aims at the problem of autonomous operation of unmanned electric shovel,establishes a dynamic model of the working mechanism according to the operating characteristics of the electric shovel,and then proposes a multi-objective optimization design method for the excavation trajectory of intelligent operation;On this basis,an intelligent system that integrates perception,decision-making and control and can complete excavation operations autonomously is designed,and the trajectory performance of theoretical planning is verified through simulation and experiment.The specific research contents are as follows:1)According to the excavation requirements of shovel,a multi-objective trajectory planning method is proposed.First,a dynamic equation for the simplified working mechanism is established and the key parameters of the excavation process are analyzed to prepare for trajectory planning.At the same time,the multi-objective optimization model of the excavation trajectory is established by using the polynomial interpolation method with the goal of minimum excavation energy consumption and minimum back angle difference,LHS method is introduced on the SQP algorithm to optimize to meet the rapid planning of the shovel excavation trajectory.2)According to the requirements of the autonomous operation of the electric shovel,an autonomous excavation system for perception,decision-making and control is built based on the ROS system to realize the real-time perception of the external environment and autonomous planning of intelligent mining operations.An excavation environment model base on lidar is built in the perception system to realize that the electric shovel can adapt to different environments for excavation.The real-time communication mechanism of data flow between the shovel multi-node hardware is designed in the decision system to realize the real-time monitoring of excavation status and the real-time transmission and exchange of information of each node.In the control system,the underlying driver control with STM32 microcontroller as the main control is established to realize the operation task of the system.Finally,an intelligent forward console is built to realize the friendly human-machine interaction and visual status monitoring of the electric shovel.3)In order to realize intelligent experimental research,a URDF model for the electric shovel is established firstly in the ROS environment,the excavation process is simulated and compared with numerical simulation to verify the reasonable motion relationship of the joint through Rviz and Moveit!.Then,in order to verify the effectiveness of the trajectory planning method and the autonomous excavation system,they are applied to the experimental prototype for testing.The results show that the autonomous excavation process has lower energy consumption,higher efficiency,continuous and more stable excavation process than manual operation.And,corresponding to different excavation environments,there is an optimal excavation trajectory,which is of great significance to the intelligent research of unmanned electric shovel. |
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