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Determination of optimal measurement configurations for self-calibrating a robotic visual inspection system with multiple point constraints

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Abstract

In this paper, we propose an algorithm to determine optimal measurement configurations for self-calibrating a robotic visual inspection system with multiple point constraints. The algorithm aims to improve the robotic visual inspection system’s calibration accuracy. To do so, a pre-calibration of the robotic visual inspection system is needed to obtain the hand-eye and robot exterior relationship to implement the inverse kinematic algorithm. The candidate measurement configurations with one point constraint can be obtained using the inverse kinematic algorithm for the robotic visual inspection system, so DETMAX is implemented to determine a given number of optimal measurement configurations from the candidate measurement configurations. Particle swarm optimization is used to optimize the positions of the multiple points one by one. To verify the efficiency of the proposed approach, experiment evaluation is conducted on a robotic visual inspection system.

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Funding

This work is supported by the National Natural Science Foundation of China (51575354), the National Key Technology Research and Development of the Ministry of Science and Technology of China (2012BAF12B01, 973 Program 2014CB046604), and the Shanghai Municipal Science and Technology project (16111106102).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao tong University, Shanghai, China

    Chengyi Yu & Xiaobo Chen

  2. The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao tong University, Shanghai, China

    Juntong Xi

Authors
  1. Chengyi Yu
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  2. Xiaobo Chen
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  3. Juntong Xi
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Correspondence to Juntong Xi.

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Yu, C., Chen, X. & Xi, J. Determination of optimal measurement configurations for self-calibrating a robotic visual inspection system with multiple point constraints. Int J Adv Manuf Technol 96, 3365–3375 (2018). https://doi.org/10.1007/s00170-018-1739-x

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  • DOI: https://doi.org/10.1007/s00170-018-1739-x

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