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2017 Volume 32
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RESEARCH ARTICLE   Open Access    

A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller

  • aiRobots Lab

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  • Abstract: Obstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.
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  • Cite this article

    Ping-Huan Kuo, Tzuu-Hseng S. Li, Guan-Yu Chen, Ya-Fang Ho, Chih-Jui Lin. 2017. A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000151
    Ping-Huan Kuo, Tzuu-Hseng S. Li, Guan-Yu Chen, Ya-Fang Ho, Chih-Jui Lin. 2017. A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000151
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RESEARCH ARTICLE   Open Access    

A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller

  • aiRobots Lab

  • Published online: 09 September 2016
The Knowledge Engineering Review  32,  2017, 32(1)  |  Cite this article

Abstract: Abstract: Obstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.

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    Acknowledgments

    • This work was supported in part by the Ministry of Science and Technology, Taiwan, ROC, under grants MOST 103-2221-E-006-252 and MOST 104-2221-E-006-228-MY2, and in part by the Ministry of Education, Taiwan, within the Aim for the Top University Project through National Cheng Kung University, Tainan, Taiwan.

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    Ping-Huan Kuo, Tzuu-Hseng S. Li, Guan-Yu Chen, Ya-Fang Ho, Chih-Jui Lin. 2017. A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000151
    Ping-Huan Kuo, Tzuu-Hseng S. Li, Guan-Yu Chen, Ya-Fang Ho, Chih-Jui Lin. 2017. A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000151
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