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Survey of research progress on crowdsourcing task assignment for evaluation of workers’ ability
MA Hua, CHEN Yuepeng, TANG Wensheng, LOU Xiaoping, HUANG Zhuoxuan
Journal of Computer Applications 2021, 41 (
8
): 2232-2241. DOI:
10.11772/j.issn.1001-9081.2020101629
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357
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With the rapid development of internet technology and sharing economy mode, as a new crowd computing mode, crowdsourcing has been widely applied and become a research focus recently. Aiming at the characteristics of crowdsourcing applications, to ensure the completion quality of crowdsourcing tasks, the existing researches have proposed different crowdsourcing task assignment methods from the perspective of the evaluation of worker's ability. Firstly, the crowdsourcing's concept and classification were introduced, and the workflow and task characteristics of the crowdsourcing platform were analyzed. Based on them, the existing research works on the evaluation of workers' ability were summarized. Then, the crowdsourcing task assignment methods and the related challenges were reviewed from three different aspects, including matching-based, planning-based and role-based collaboration. Finally, the research directions of future work were put forward.
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Robot hand-eye calibration by convex relaxation global optimization
LI Wei, LYU Naiguang, DONG Mingli, LOU Xiaoping
Journal of Computer Applications 2017, 37 (
5
): 1451-1455. DOI:
10.11772/j.issn.1001-9081.2017.05.1451
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Hand-eye calibration based on nonlinear optimization algorithm can not guarantee the convergence of the objective function to the global minimum, when there are errors in both robot forward kinematics and camera external parameters calibration. To solve this tricky problem, a new hand-eye calibration algorithm based on quaternion theory by convex relaxation global optimization was proposed. The critical factor of the angle between different interstation rotation axes by a manipulator was considered, an optimal set of relative movements from calibration data was selected by Random Sample Consensus (RANSAC) approach. Then, rotation matrix was parameterized by a quaternion, polynomial geometric error objective function and constraints were established based on Linear Matrix Inequality (LMI) convex relaxation global optimization algorithm, and the hand-eye transformation matrix could be solved for global optimum. Experimental validation on real data was provided. Compared with the classical quaternion nonlinear optimization algorithm, the proposed algorithm can get global optimal solution, the geometric mean error of hand-eye transformation matrix is no more than 1.4 mm, and the standard deviation is less than 0.16 mm.
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