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Pin Lv

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

EAAI Journal 2024 Journal Article

Positional error compensation for aviation drilling robot based on Bayesian linear regression

  • DongDong Chen
  • Pin Lv
  • Lei Xue
  • Hongwen Xing
  • Lixin Lu
  • Dongdong Kong

The quality of aircraft components is directly affected by the positional accuracy of aviation drilling robot, which may further affect the assembly accuracy, flight performance and service life of aircraft. In this work, positional error compensation based on Bayesian linear regression (BLR) is proposed to improve the positional accuracy of aviation drilling robot. Firstly, the stochastic model of robot positional error is constructed by utilizing kinematic error model involving with random uncertainties. It proves that the positional error of robot joint space follows the Gaussian process (GP) and can be modeled through a linear regression model. Secondly, BLR is utilized to construct the positional error model of aviation drilling robot, which can provide the predicted positional error and the corresponding confidence interval (CI) of target position. These predicted values and CIs are used to realize error compensation. Experimental results show that, after error compensation, the average/maximum absolute positional error of the self-developed aviation drilling robot is decreased from 1. 393mm/1. 795 mm–0. 081mm/0. 167 mm. This indicates that the BLR-based positional error compensation is conductive to ameliorating the positional accuracy of aviation drilling robot. This study lays the foundation for the positional error compensation of industrial robots in aircraft assembly.

Details DOI

EAAI Journal 2009 Journal Article

Cloud theory-based simulated annealing algorithm and application

  • Pin Lv
  • Lin Yuan
  • Jinfang Zhang

Using the randomness and stable tendency of a Y condition normal cloud generator, a cloud theory-based simulated annealing algorithm (CSA) is originally proposed, whose characteristic is approximately continuous decrease in temperature and implied “Backfire & Re-Annealing”. It fits the annealing process of solid matter in nature much better, overcomes the traditional simulated annealing algorithm (SA)'s disadvantages, which are slow searching speed and being trapped by local minimum easily, then enhances the veracity of final solution and reduces the time cost of the optimization process simultaneously. Theory analysis proves that CSA is convergent and typical function optimization experiments show that CSA is superior to SA in terms of convergence speed, searching ability and robustness. The result of the application using CSA for multiple observers sitting problem (MOST) in visibility-based terrain reasoning (VBTR) also declares the new algorithm's usefulness and effectiveness adequately.

Details DOI
v2026.09.13