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On Global Smooth Path Planning for Mobile Robots using a Novel Multimodal Delayed PSO Algorithm

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Abstract

The planning problem for smooth paths for mobile robots has attracted particular research attention, but the strategy combining the heuristic intelligent optimization algorithm (e.g., particle swarm optimization) with smooth parameter curve (e.g., Bezier curve) for global yet smooth path planning for mobile robots has not been thoroughly discussed because of several difficulties such as the local trapping phenomenon in the searching process. In this paper, a novel multimodal delayed particle swarm optimization (MDPSO) algorithm is developed for the global smooth path planning for mobile robots. By evaluating the evolutionary factor in each iteration, the evolutionary state is classified by equal interval division for the swarm of the particles. Then, the velocity updating model would switch from one mode to another according to the evolutionary state. Furthermore, in order to reduce the occurrence of local trapping phenomenon and expand the search space in the searching process, the so-called multimodal delayed information (which is composed of the local and global delayed best particles selected randomly from the corresponding values in previous iterations) is added into the velocity updating model. A series of simulation experiments are implemented on a standard collection of benchmark functions. The experiment results verify that the comprehensive performance of the developed MDPSO algorithm is superior to other well-known PSO algorithms. Finally, the presented MDPSO algorithm is utilized in the global smooth path planning problem for mobile robots, which further confirms the advantages of the MDPSO algorithm over the traditional genetic algorithm (GA) investigated in previous studies. The multimodal delayed information in the MDPSO reduces the occurrence of local trapping phenomenon and the convergence rate is satisfied at the same time. Based on the testing results on a selection of benchmark functions, the MDPSO’s performance has been shown to be superior to other five well-known PSO algorithms. Successful application of the MDPSO for planning the global smooth path for mobile robots further confirms its excellent performance compared with the some typical existing algorithms.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Baoye Song, Zidong Wang & Lei Zou

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang

Authors
  1. Baoye Song
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Correspondence to Zidong Wang.

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The authors declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human or animal subjects performed by the any of the authors.

Additional information

This work was supported in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China and the Higher Educational Science and Technology Program of Shandong Province of China under Grant J14LN34.

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Song, B., Wang, Z. & Zou, L. On Global Smooth Path Planning for Mobile Robots using a Novel Multimodal Delayed PSO Algorithm. Cogn Comput 9, 5–17 (2017). https://doi.org/10.1007/s12559-016-9442-4

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