Research On Trajectory Planning And Control Of Excavator Working Device

Excavators are one of the most common types of engineering machinery in today’s society.They are widely used in various fields such as mining construction,engineering construction,and emergency rescue operations due to their high performance and efficiency.With the rapid development of modern society,the operational tasks faced by excavators have become more complex and diverse,and the demands for the quality of excavator operations have become more stringent.However,traditional backhoe hydraulic excavators rely solely on manual operation by workers during the operation process,which seriously affects the efficiency and quality of work in harsh environments or long working hours.Additionally,the traditional work device system has problems such as control lag and low control accuracy.Therefore,the automation and intelligence of excavators have great potential.Currently,research on the intelligence of excavators mainly focuses on the operation speed,operation energy consumption,and trajectory tracking control accuracy.The former depends on reasonable trajectory planning technology,while the latter depends on high-precision trajectory tracking control technology.This paper will focus on these two aspects of research.The main research contents of this article are as follows:(1)Establish the kinematic model of the excavator working device.The mapping relationship between the joint angles of the excavator working device,the position and orientation of the bucket tip is obtained using the D-H method.The kinematic inverse solution is obtained using geometric method to describe the position and orientation of the bucket tip in the pose space,joint space and drive space.(2)In the process of excavator operation,the trajectory planning is not reasonable and the impact is large,In order to solve the problem of excavator working track planning and optimization.Taking the common excavation condition of deep pit excavation as the object,a trajectory optimization method considering the efficiency and energy consumption of excavator operation is proposed based on the 4-3-3-3-4segmented polynomial trajectory planning method.The trajectory of each joint of the working device is optimized.The single operation time and the average angular acceleration are used as evaluation indexes,and the joint angular velocity and angular acceleration are used as constraints.A multi-objective optimization algorithm is used to optimize the excavator operation trajectory.(3)In order to improve the operation accuracy of the excavator working device,the control system of the excavator working device consisting of the arm,bucket rod and bucket is analyzed,and the transfer functions of each system of the excavator working device are established.A mathematical model is established using the Simulink module in MATLAB,and a PID controller is introduced for trajectory tracking control.Tracking simulation experiments with unit sinusoidal inputs are performed on each control system.(4)Aiming at the problems of low tracking accuracy and poor effect of excavator working device,In order to improve the control effect of the working device,artificial intelligence technology is combined,and the BP neural network algorithm is introduced to adjust the PID controller parameters,and the PSO algorithm is used to optimize the BP＿PID controller to improve the system response rate and control accuracy.An improved BP＿PID controller is designed,and its effectiveness is verified by joint simulation experiments,which demonstrate that the tracking error is maintained within8 mm.This paper focuses on the trajectory planning and tracking control of excavator operation,with the goal of optimizing efficiency and energy consumption.Innovative optimization methods are proposed to obtain the optimal operation trajectory that meets the requirements.Intelligent control algorithms are combined to achieve precise control of the system,ensuring accurate tracking of the trajectory during the operation process.The trajectory optimization method and tracking control algorithm proposed in this paper have achieved good results,providing technical support for the research and application of excavator autonomous operation.