Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-27T09:38:28.650Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  09 September 2016

Ping-Huan Kuo ,
Tzuu-Hseng S. Li ,
Guan-Yu Chen ,
Ya-Fang Ho  and
Chih-Jui Lin
Ping-Huan Kuo
Affiliation:
aiRobots Lab, Department of Electrical and Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Tzuu-Hseng S. Li
Affiliation:
aiRobots Lab, Department of Electrical and Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Guan-Yu Chen
Affiliation:
aiRobots Lab, Department of Electrical and Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Ya-Fang Ho
Affiliation:
aiRobots Lab, Department of Electrical and Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Chih-Jui Lin
Affiliation:
aiRobots Lab, Department of Electrical and Electronic Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC e-mail: [email protected], [email protected], [email protected], [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

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.

Type
Review Article
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e5
Copyright
© Cambridge University Press, 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Campos, M., Krohling, R. A. & Enriquez, I. 2014. Bare bones particle swarm optimization with scale matrix adaptation. IEEE Transactions on Cybernetics 44(9), 1567–1578.CrossRefGoogle ScholarPubMed
Chen, C.-H., Liu, T.-K. & Chou, J.-H. 2013. Integrated short-haul airline crew scheduling using multiobjective optimization genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(5), 1077–1090.CrossRefGoogle Scholar
Duman, S., Güvenç, U., Sönmez, Y. & Yörükeren, N. 2012. Optimal power flow using gravitational search algorithm. Energy Conversion and Management 59, 86–95.CrossRefGoogle Scholar
FIRA Homepage 2014. FIRA HuroCup Rules. http://www.fira.net/contents/sub03/sub03_1.asp.Google Scholar
Holland, J. H. 1992. Genetic algorithms. Scientific American 267(1), 66–72.Google Scholar
Jan, G. E., Sun, C. C., Tsai, W. C. & Lin, T. H. 2014. An O (n log n) shortest path algorithm based on Delaunay triangulation. IEEE/ASME Transactions on Mechatronics 19(2), 660–666.CrossRefGoogle Scholar
Kennedy, J. & Eberhart, R. 1992. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, 66–72.Google Scholar
Konar, A., Chakraborty, I. G., Singh, S. J., Jain, L. C. & Nagar, A. K. 2013. A deterministic improved Q-learning for path planning of a mobile robot. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(5), 1141–1153.Google Scholar
Koren, Y. & Borenstein, J. 1991. Potential field methods and their inherent limitations for mobile robot navigation. In Proceedings of the 1991 IEEE International Conference on Robotics and Automation, 1398–1404.Google Scholar
Kuo, P.-H. & Li, T.-H. S. 2011. Development of simulator for AndroSot in FIRA. In Proceedings of the FIRA 2011, CCIS 212, 233–240.Google Scholar
Lu, W., Zhang, G. & Ferrari, S. 2014. An information potential approach to integrated sensor path planning and control. IEEE Transactions on Robotics 30(4), 919–934.CrossRefGoogle Scholar
Rashedi, E., Nezamabadi-Pour, H. & Saryazdi, S. 2009. GSA: a gravitational search algorithm. Information Sciences 179(13), 2232–2248.Google Scholar
Shaw, B., Mukherjee, V. & Ghoshal, S. P. 2012. A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems. International Journal of Electrical Power & Energy Systems 35(1), 21–33.Google Scholar
Shimoda, S., Kuroda, Y. & Iagnemma, K. 2005. Potential field navigation of high speed unmanned ground vehicles on uneven terrain. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2839–2844. http://ieeexplore.ieee.org/document/1570542/.Google Scholar
Sun, X., Gong, D., Jin, Y. & Chen, S. 2013. A new surrogate-assisted interactive genetic algorithm with weighted semisupervised learning. IEEE Transactions on Cybernetics 43(2), 685–698.Google Scholar
Tu, K.-Y. & Baltes, J. 2006. Fuzzy potential energy for a map approach to robot navigation. Robotics and Autonomous Systems 54(7), 574–589.CrossRefGoogle Scholar
Weindler, P., Wiltschko, R. & Wiltschko, W. 1996. Magnetic information affects the stellar orientation of young bird migrants. Nature 383, 158–160.Google Scholar
Yazici, A., Kirlik, G., Parlaktuna, O. & Sipahioglu, A. 2014. A dynamic path planning approach for multirobot sensor-based coverage considering energy constraints. IEEE Transactions on Cybernetics 44(3), 305–314.CrossRefGoogle ScholarPubMed
Yoon, Y. & Kim, Y.-H. 2013. An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Transactions on Cybernetics 43(5), 1473–1483.Google Scholar
Zhigang, R., Aimin, Z., Changyun, W. & Zuren, F. 2014. A scatter learning particle swarm optimization algorithm for multimodal problems. IEEE Transactions on Cybernetics 44(7), 1127–1140.Google Scholar