Dynamic parameter identification for the CRS A460 robot

Radkhah, Katayon; Kulić, Dana; Croft, Elizabeth A.

doi:10.1109/iros.2007.4399314

Cited by 32 publications

(16 citation statements)

References 8 publications

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“…In order to design a controller for a robot, a precise model of the robotic system is required with accurate information on dynamic parameters The trajectories for each joint were generated using Fourier series and fed into the robot controller to collect two sets of identification data, one at full speed and the other at half speed. Radkhah et al [14] set out to identify the dynamic parameters of a CRS A460 robot. Fourier series parameters were used to generate the joint trajectories to identify the dynamic parameters.…”

Section: Kinematic and Dynamic Modellingmentioning

confidence: 99%

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Robotic Sanding of Wooden Bowls with Hybrid Force/Position Impedance Control

Sudhagar

Surgenor

Hashtrudi-Zaad

2020

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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This thesis set out to determine whether a serial robot could be used to sand a wooden bowl, in order to free a human operator from what is considered a hazardous task. The process of sanding wood is similar to the polishing of metal, both set out to eliminate scratches. There are robot-based commercial systems available for the polishing of metal, for example aluminum, as employed by the aerospace industry. However, unlike aluminum, wood is a non-homogeneous material. In the case of wooden bowls, each has a unique geometry, and to a degree, unique material properties. The hypothesis posed by this thesis is that a hybrid force/position impedance controller can sand the inside of a wooden bowl and mimic the motions and technique of a human operator, and yet be able to deal with conditions of unknown bowl geometry. A CRS A465 robotic arm was used to mimic the arm motion of a human operator. The bowl was held in place by a vacuum chuck. A pneumatic orbital sander was used for the sanding process. A 3-axis force sensor mounted on the end effector measured the applied force. The arm was programmed to follow a radial line from the rim of the bowl to its center and then back to the rim. A hybrid force/position impedance controller was implemented. A forward and inverse kinematic model of the robot was developed to enable trajectory generation. The effect of sander angle and the nature of the trajectory was tested. PD action was adopted for the position controller. Four different force controllers were tested: First Order (FO) filter, Butterworth filter, PI action and combined FO/PI. Tuning tests were conducted to obtain the best values for the controller gains. The conclusion is that a FO filter for the force controller and PD action for the position controller, with an appropriate settings for the impedance, gave the best control performance. This performance was obtained with only a rough estimation of the bowl geometry as based on the bowl's diameter and depth. A precise CAD/CAM model of the bowl was not employed. iii ACKNOWLEDGEMENTS I would like to express my deepest gratitude to my supervisor, Dr. Brian Surgenor for his full support, expert guidance, understanding and incredible patience throughout my study and research. I have learned a lot from him throughout the completion of this thesis work. I would also like to thank my co-supervisor Prof. Keyvan Hashtrudi-Zaad and PdF. Chiedu Mokogwu for helping me to set up the CRS robot and force sensor. I would like to acknowledge the work of my friend: Hamza Sheikh. Thank you for your help with transferring inverse kinematic equations to the Simulink. I thank my lab mates Victor Luna, Andres Ramos, Shane Forbrigger and Orighomisan Mayuku for their support with moving equipment and testing of the robot. Thank you all for your helpful ideas, enthusiasm and friendship during the last two years. The financial support provided by the School of Graduate Studies and Department of Mechanical and Materials Engineering is greatly acknowledged. I must express my gratitude for my p...

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Section: Kinematic and Dynamic Modellingmentioning

confidence: 99%

“…In this section, differential kinematics are used to compute the "Jacobian" matrix which is used to map the joint velocities to the respective end-effector's linear and angular velocities. The conversion of joint velocity to end-effector angular/linear velocity can be expressed as (Croft et al [14]):…”

Section: Differential Kinematicsmentioning

confidence: 99%

Robotic Sanding of Wooden Bowls with Hybrid Force/Position Impedance Control

Sudhagar

Surgenor

Hashtrudi-Zaad

2020

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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“…The dynamic parameters of a 6DOF manipulator was estimated in two stages, the last three links corresponding to the wrist were first estimated followed by the first three links corresponding to the arm of the manipulator. A numeric technique for reducing the observation matrix to a minimum linear combination of identifiable parameters was implemented as described in [40] where three empty matrices were created corresponding to identifiable parameters, unidentifiable parameters, and identifiable parameters in linear combinations. Understanding that the rank of the observation matrix is equal to the number of identifiable parameters, the norm of each column of the observation matrix is determined independently.…”

Section: Parameter Estimationmentioning

confidence: 99%

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

Umar¹,

Shi²,

Zheng³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…There is a number of methodologies to determine both the parameters influencing the systems dynamic and the set of minimum parameters required to identify the models of such systems (Choi, Yoon, Park, & Kim, 2011;Mayeda, Yoshida, & Ohashi, 1989;Mayeda, Yoshida, & Osuka, 1990;Radkhah, Kulic, & Croft, 2007). This work considers a numerical method that allows for identifying the set of independent parameters of the redundant manipulator robot, based on the analysis of the kernel of the regressor.…”

Section: Least Squaresmentioning

confidence: 99%

Parameter identification methods for real redundant manipulators

Urrea¹,

Pascal²

2017

Journal of Applied Research and Technology

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This work presents the development, assessment and comparison of four techniques for identifying dynamic parameters in an industrial redundant manipulator robot with 5 degrees of freedom. Based on the Lagrange-Euler formulation, a linear model of the robot with unknown parameters is obtained. Then, these parameters are identified using the following techniques: least squares, artificial Adaline neural networks, artificial Hopfield neural networks and extended Kalman filter. The parameters identified are validated by using them for computationally simulating the performance of the redundant manipulator robot, to which are imposed reference trajectories different from the ones used in the estimation. To relate the trajectories performed by the redundant manipulator robot with the estimated parameters, the following error indexes are calculated: Residual Mean Square, Residual Standard Deviation and Agreement Index. Finally, to determine the sensitivity of the model identified-due to the variations of the estimated parameters-a new simulation is conducted on the robot, considering that its parameters vary in a restricted range. In addition, the RMS error index of the trajectories performed is determined. After this step, the parameters of the redundant manipulator robot were successfully identified and, thus, its mathematical model was known.

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Dynamic parameter identification for the CRS A460 robot

Cited by 32 publications

References 8 publications

Robotic Sanding of Wooden Bowls with Hybrid Force/Position Impedance Control

Robotic Sanding of Wooden Bowls with Hybrid Force/Position Impedance Control

Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning

Parameter identification methods for real redundant manipulators

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