Path planning of mobile robot based on hybrid improved artificial fish swarm algorithm
Yi Zhang1 , Yuanhong Hua2
1, 2Center of Chongqing Information Accessibility and Service Robot Engineering Technology Research, Chongqing, 400065, China
Vibroengineering PROCEDIA, Vol. 17, 2018, p. 130-136.
Received 26 February 2018; received in revised form 7 March 2018; accepted 11 March 2018; published 20 April 2018
The artificial fish swarm algorithm is easy to fall into the local optimum for robot global path planning. A hybrid improved Artificial Fish Swarm Algorithm (HIAFSA) is proposed. Firstly, the sub-optimal path is determined by A* algorithm, and then the adaptive behavior of artificial fish swarm algorithm is improved based on the inertia weight factor, and the attenuation function is introduced to improve the visual range and moving step length of the artificial fish, balance the global path planning and local path planning, and further improve the convergence speed and quality of the solution. The experimental results show that the hybrid improved artificial fish swarm algorithm has been improved in avoiding local optimum, convergence speed and precision.
Keywords: path planning, A* algorithm, artificial fish swarm algorithm, attenuation function, inertia weight factor.
This paper belongs to the Project of the “Chongqing Science and Technology Committee, China” No. cstc2015jcyjBX0066 and “Research Project of Chongqing Municipal Education Commission Science and Technology” No. KJ1600442.
- Li P., Huang X., Wang M. A new hybrid method for mobile robot dynamic local path planning in unknown environment. Journal of Computers, Vol. 5, Issue 5, 2010, p. 773-781. [Publisher]
- Lai L. C., Lu C. F., Chang Y. C., et al. Position estimation of a mobile robot by PSO algorithm using a laser range finder. International Conference on Consumer Electronics, Communications and Networks, 2011, p. 1505-1508. [CrossRef]
- Peng J. S. The robot path optimization of improved artificial fish-swarm algorithm. Computer Modelling and New Technologies, Vol. 18, Issue 6, 2014, p. 147-152. [CrossRef]
- Wang J., Wu L. J. Robot path planning based on artificial fish swarm algorithm under a known environment. Advanced Materials Research, Vols. 989-994, 2014, p. 2467-2469. [CrossRef]
- Li G. Q., Yang Zhao Y. W. F. Q., et al. Parallel adaptive artificial fish swarm algorithm based on differential evolution. Proceedings of the 9th International Symposium on Computational Intelligence and Design, 2016, p. 269-273. [CrossRef]
- Fei T., Zhang L. Y., Bai Y., et al. Improved artificial fish swarm algorithm based on DNA. Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Vol. 49, Issue 6, 2016, p. 581-588. [CrossRef]
- Zhang C., Zhang F. M., Li F., et al. Improved artificial fish swarm algorithm. Industrial Electronics and Applications, 2014, p. 748-753. [CrossRef]
- Qian J., Zhou Z., Zhao L., et al. An improved reconfiguration algorithm for VLSI arrays with A-star. International Conference on Computational Science and Its Applications, 2016. [CrossRef]
- Chang Hua Cheng, Chen Hung Yuan Exploration of action figure appeals using evaluation grid method and quantification theory type I. Eurasia Journal of Mathematics Science and Technology Education, Vol. 13, Issue 5, 2017, p. 1445-1459. [Publisher]
- Xu Y., Liu R. Path planning for mobile articulated robots based on the improved A* algorithm. International Journal of Advanced Robotic Systems, Vol. 14, Issue 4, 2017, https://doi.org/10.1177/1729881417728013. [Publisher]
- Luan X. Y., Li Z. P., Liu T. Z. A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm. Neurocomputing, Vol. 174, 2016, p. 522-529. [Publisher]