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A grey probability measure set based mobile robot position estimation algorithm

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  • Robotics and Automation
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Abstract

A novel algorithm for the position estimation of a mobile robot is proposed, which is based on probability statistics and grey system theory. For the proposed algorithm, the grey probability measure set is established, which is composed of a base set and the corresponding grey probability measure. The base set is used to represent the uncertain information and the grey probability measure distributes probability on the base set. Moreover, the integrating rules are formulated using the grey probability measure set and the q-satisfied rule to estimate the position of a mobile robot. In addition to providing a new way of representing the uncertain information, results of the proposed algorithm are also more reliable in the presence of error and outliers. The algorithm is applied in the position estimation of a Pioneer 3-DX robot in a corridor-office environment. Experimental results have shown that the estimation accuracy of the algorithm is as good as that of the particle filter and better than that of the extended Kalman filter.

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Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, West Campus, Hefei, Anhui, 230027, P. R. China

    Peng Wang, Qi-Bin Zhang & Zong-Hai Chen

Authors
  1. Peng Wang
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  2. Qi-Bin Zhang
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  3. Zong-Hai Chen
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Corresponding author

Correspondence to Zong-Hai Chen.

Additional information

Recommended by Associate Editor Hyun Myung under the direction of Editor Myotaeg Lim.

This paper was supported by the National Natural Science Foundation of China (Grant No. 61375079).

Peng Wang received his B.S. degree from the University of Science and Technology of China (USTC) in 2010. He is now a Ph.D. candidate at the Laboratory of Simulation and Intelligent Control in the Department of Automation, USTC. His research interests include system modeling and simulation, mobile robot localization, uncertain information processing and knowledge representation.

Qi-Bin Zhang received his B.S. degree from the University of Science and Technology of China (USTC) in 2012. He is now a Ph.D. candidate at the Laboratory of Simulation and Intelligent Control in the Department of Automation, USTC. His research interests include mobile robot localization and landmark extraction, knowledge representation and interval analysis.

Zong-Hai Chen received his B.S. degree from the University of Science and Technology of China (USTC) in 1988. He is currently a professor at the Laboratory of Simulation and Intelligent Control in the Department of Automation, USTC. His research interests include simulation and optimization control of complex system, theory and technology of intelligent system, and quantum control theory. Prof. Chen is now a member of the Robotics Technical Committee of the International Federation of Automatic Control (IFAC).

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Wang, P., Zhang, QB. & Chen, ZH. A grey probability measure set based mobile robot position estimation algorithm. Int. J. Control Autom. Syst. 13, 978–985 (2015). https://doi.org/10.1007/s12555-014-0149-6

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  • DOI: https://doi.org/10.1007/s12555-014-0149-6

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