Error Analysis And Compensation Of Manipulator Of Lunar Sampling Simulation Platform

With the advancement of China’s lunar exploration missions,lunar surface sampling technology has become an important research component of current deep space exploration missions.At this stage,lunar sampling is generally done by robotic arm,and its whole sampling work is carried out in remote operation mode with strict control accuracy requirements.The factors affecting the control accuracy of the sampling robotic arm not only come from the machining and manufacturing accuracy of the robotic arm and the control procedure,but also include its own flexible deformation caused by the lightweight and slender arm structure design requirements,which brings great challenges to the improvement of the robotic arm control accuracy.Based on the lunar surface sampling simulation platform in laboratory,this paper proposes a graded error compensation strategy considering both geometric and flexible errors,unifies geometric and flexible errors into the same error compensation framework,constructs an error prediction model,designs different compensation strategies,and realizes the positioning accuracy errors brought by inaccurate geometric parameters of the robotic arm and The error prediction model is constructed and different compensation strategies are designed to achieve the accurate compensation of the positioning accuracy error and the flexibility error caused by the inaccurate geometric parameters of the robot arm and the flexibility error caused by the flexible joint and flexible linkage.The main research contents of this paper are as follows.(1)In order to simulate the positioning error of the sampling robot arm caused by the geometric parameter error,the calibration method of the robot arm geometric parameters is studied.The MDH model is used to derive the mapping relationship between the parameter error of each linkage of the robot arm and the end position error,and a parameter identification model is established.In order to identify all the parameter errors,the parameter identification is transformed into a multi-parameter optimization problem,and a particle swarm algorithm is introduced for parameter calibration.The simulation results show that both methods can accurately solve the kinematic parameter errors of the robot arm and improve the positioning accuracy.(2)The error prediction method based on the stiffness model is proposed for the problem of control accuracy degradation caused by the weak stiffness of the slender structure simulation sampling robot arm.The overall stiffness model is established by analyzing the joint flexibility,arm flexibility and self-weight on the end position of the robot arm,and the correctness and applicability of the model are verified by finite element analysis and real experimental comparison.(3)In order to improve the control accuracy of the robot arm,a graded error compensation strategy is proposed according to the error sources of the robot arm,which can correct the positioning error and end deformation error predicted by the stiffness model through compensating the inaccurate geometric parameters of the robot arm.The simulation experiment verifies that the strategy can calculate the compensation value to meet the accuracy at one time,which greatly improves the control accuracy of the simulated sampling robotic arm.Since the sampling work is in remote operation mode and requires fine adjustment of small distances,a translation trajectory planning algorithm based on spatial linear interpolation is proposed,and the feasibility and practicality of the proposed algorithm are verified by simulation experiments and experiments of can grasping by the robotic arm of the lunar surface sampling simulation platform in laboratory.