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Trajectory planning for autonomous mobile robot using a hybrid improved QPSO algorithm

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Abstract

This paper proposes a hybrid improved quantum-behaved particle swarm optimization (LTQPSO) for autonomous mobile robot (AMR) trajectory planning in the environment with random obstacles. The algorithm combines the individual particle evolutionary rate and the swarm dispersion with natural selection method in particle evolution process. It is tested on several benchmark functions and proved that the convergence capability and accuracy are better than conventional QPSO, WQPSO and IQPSOS algorithms. To assess the effectiveness and feasibility of the proposed method on real problems, it is applied to the trajectory planning for AMR in the environment with random obstacles. The relationships between basic parameters are analyzed and formulated according to initial distribution of the LTQPSO. To show the high capability of the improved method, the LTQPSO is compared with QPSO, WQPSO and IQPSOS in the aspects of solution quality, robustness and convergence property. Experimental results demonstrate that the modified LTQPSO is very effective.

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Acknowledgments

This research was sponsored in part by the Ministry of Science and Technology Fund Project (Contract No. 2015DFA81640) and Aeronautical Science Foundation of China (Contract No. 20130179002) at the Huazhong University of Science and Technology.

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Authors and Affiliations

  1. Digital Manufacturing Equipment and Technology National Key Laboratory, School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Tao Xue, Renfu Li & Myongchol Tokgo

  2. School of Mechanical Science and Technology, Kim Chaek University of Technology, Pyongyang, 999093, Democratic People’s Republic of Korea

    Myongchol Tokgo, Junchol Ri & Gyanghyok Han

Authors
  1. Tao Xue
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  2. Renfu Li
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  3. Myongchol Tokgo
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  4. Junchol Ri
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  5. Gyanghyok Han
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Correspondence to Renfu Li.

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Communicated by V. Loia.

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Xue, T., Li, R., Tokgo, M. et al. Trajectory planning for autonomous mobile robot using a hybrid improved QPSO algorithm. Soft Comput 21, 2421–2437 (2017). https://doi.org/10.1007/s00500-015-1956-2

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