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Research Papers

Calibration of Modular Reconfigurable Robots Based on a Hybrid Search Method

[+] Author and Article Information
Yu Lin1

Department of Aerospace Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canadayu.lin@ryerson.ca

Fengfeng Xi

Department of Aerospace Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canadafengxi@ryerson.ca

Richard Phillip Mohamed

Department of Aerospace Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canadar3mohame@ryerson.ca

Xiao-wei Tu

Department of Aerospace Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada; Aerospace Manufacturing Technology Center, National Research Council Canada, Montreal, QC, H3T 2B2, Canadaxiao-wei.tu@cnrc-nrc.gc.ca

1

Corresponding author

J. Manuf. Sci. Eng 132(6), 061002 (Oct 15, 2010) (8 pages) doi:10.1115/1.4002586 History: Received May 14, 2010; Revised September 07, 2010; Published October 15, 2010; Online October 15, 2010

Developed in this paper is a hybrid method for calibration of modular reconfigurable robots (MRRs). The underlying problem under study is unique to MRRs, that is, how to calibrate a set of MRR’s geometric parameters that are applicable to all feasible configurations. For this reason, a hybrid search method is developed to ensure a global search over the MRRs’ workspace for each feasible configuration. By combining a genetic algorithm method with a Monte Carlo method, this method includes three levels of search, namely, pose, workspace, and configuration-space. The final set of global solutions is generated progressively from the results of these three levels of search. The effectiveness of this method is demonstrated through a case study.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

Multibody modeling for MRRs with static and motion parts

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Figure 2

Revolute modules with three different configuration set-ups and MRR configuration space enumerated with three revolute modules (21)

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Figure 3

A hybrid search method for calibration of MRRs consisting of a pose level, workspace, and configuration-space searches

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Figure 9

Mean values and standard deviation of the end-effector pose errors at 32 poses for 37 solutions. Solutions 1-32 are the pose level solutions or the local solutions at each pose; Solutions 33-36 represent the workspace solutions; whereas solution 37 indicates the configuration-space solution or global solution.

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Figure 8

The end-effector pose error at each pose when MRR-1 is calibrated using the averaged and best solutions of Δg. Averaged solution means the pose level solution, while the best solution is the best out of the ten GA runs.

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Figure 7

(a) Different fitness values for 10 GA runs with the lowest value at the ninth run; (b) identified geometric errors Δgi of the module 2 of MRR-1

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Figure 6

GA results with 20 population and 200 generations

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Figure 5

A case study: MRR-1 and configuration-space with four ICSUs (21)

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Figure 4

(a) A flowchart of the hybrid search method; (b) GA search for the robot calibration

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