Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot
Wang, Yongbo (2012-12-13)
Väitöskirja
Wang, Yongbo
13.12.2012
Lappeenranta University of Technology
Acta Universitatis Lappeenrantaensis
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-265-345-1
https://urn.fi/URN:ISBN:978-952-265-345-1
Tiivistelmä
To obtain the desirable accuracy of a robot, there are two techniques available. The first
option would be to make the robot match the nominal mathematic model. In other words, the
manufacturing and assembling tolerances of every part would be extremely tight so that all of
the various parameters would match the “design” or “nominal” values as closely as possible.
This method can satisfy most of the accuracy requirements, but the cost would increase
dramatically as the accuracy requirement increases. Alternatively, a more cost-effective
solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By
modifying the mathematical model in the controller, the actual errors of the robot can be
compensated. This is the essence of robot calibration. Simply put, robot calibration is the
process of defining an appropriate error model and then identifying the various parameter
errors that make the error model match the robot as closely as possible.
This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant
serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF
hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge
workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and
stiffness for the whole structure. The main objective of the study is to develop a suitable
calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To
this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE)
error model are developed for error modeling of the proposed robot. Furthermore, two kinds
of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov
Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the
derived error model. A measurement method based on a 3-2-1 wire-based pose estimation
system is proposed and implemented in a Solidworks environment to simulate the real
experimental validations. Numerical simulations and Solidworks prototype-model validations
are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the
calibration algorithms.
option would be to make the robot match the nominal mathematic model. In other words, the
manufacturing and assembling tolerances of every part would be extremely tight so that all of
the various parameters would match the “design” or “nominal” values as closely as possible.
This method can satisfy most of the accuracy requirements, but the cost would increase
dramatically as the accuracy requirement increases. Alternatively, a more cost-effective
solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By
modifying the mathematical model in the controller, the actual errors of the robot can be
compensated. This is the essence of robot calibration. Simply put, robot calibration is the
process of defining an appropriate error model and then identifying the various parameter
errors that make the error model match the robot as closely as possible.
This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant
serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF
hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge
workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and
stiffness for the whole structure. The main objective of the study is to develop a suitable
calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To
this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE)
error model are developed for error modeling of the proposed robot. Furthermore, two kinds
of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov
Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the
derived error model. A measurement method based on a 3-2-1 wire-based pose estimation
system is proposed and implemented in a Solidworks environment to simulate the real
experimental validations. Numerical simulations and Solidworks prototype-model validations
are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the
calibration algorithms.
Kokoelmat
- Väitöskirjat [1037]