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Research on Genetic Algorithm Apply to Identify the Robot’s Kinematics Parameters

Author: MeiGaoMing
Tutor: JiangYanZuo
School: Harbin University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Integrity Robot kinematics Pose error model Kinematic parameters identification Genetic Algorithms
CLC: TP242
Type: Master's thesis
Year: 2009
Downloads: 153
Quote: 2
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Abstract


With the development of modern industrial technology, the increasingly high demand for the end of the robot pose accuracy. Traditional industrial robots are limited to simple tasks demand, generally use offline Teach, it is mostly only concerned about the repeatability of the robot. Good reproducibility, however, does not necessarily accurately reflect the correct relationship between the end of the robot joint angle input and robot position and attitude output merely indicates that the various parts of the robot have sufficient resolution sufficient compactness and joint detection. If the high repeatability of the robot into the robot end-effector pose accuracy, you must identify the kinematic parameters of the robot - robot calibration. Firstly, for the independence of the parameters of robot kinematics, the introduction of the robot integrity, singularity and equivalence of the three concepts. Which focus on the robot integrity, and in accordance with the joint chatter or not, it is divided into the conventional model of integrity and the integrity of the generalized model. Using geometric analysis and error spatial analysis based on the derivation of the formula for the number of independent parameters of the conventional model and the generalized model. Second, for continuous joints parallel case, DH modeling method will cause the robot kinematic parameters related issues, the introduction of improved link kinematic transformation matrix derived continuous rod pose error between the model; to This is the basis for the establishment of the position and orientation of the robot end error model, and, depending on the complexity, this simplified model for the the order pose error model and second-order position and orientation error model. Based on the above position and attitude error model, to discuss the distribution of the robot end-effector pose error, calculated first-order error domain and the second error domain. Again, this paper presents a genetic algorithm based on crowding mechanism. The algorithm is based on the similarity between individuals to eliminate similar individuals in the population, in order to maintain the diversity of the population and avoid premature convergence of the algorithm. According to the position and attitude error model, taking into account the impact of the measurement noise, and in accordance with the measurement points and the number of repeated measurements by different simulation types, respectively, using a least squares algorithm and genetic algorithm parameter identification of robot kinematics simulation, simulation results show that the robot The end position and orientation accuracy has been significantly improved, to prove the effectiveness of these algorithms. The respective advantages and disadvantages of the two algorithms based on the analysis of simulation results. It is worth mentioning that the simulation: due to the randomness of the genetic algorithm search and discontinuity, the kinematic parameters of the robot is not sensitive to the genetic algorithm, strong adaptability to the kinematic model.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Robotics > Robot
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