Abstract
Ant colony algorithm is an intelligent bionic optimization algorithm. Its self-organization and intelligence provide guiding for studying the global path planning problem. Based on this, an improved ant colony algorithm is proposed to solve the problem of robotic path planning and improved the convergence speed. The environment model is established by grid method and the traditional ant colony algorithm is improved. The heuristic factor and pheromone updating strategy of the algorithm are improved to enhance the precision of the algorithm and the ability of later convergence. Simulation experiments show that the improved algorithm has a faster convergence speed to achieve the optimal path compared with other algorithms. It shows that the improved algorithm is effective and reliable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, X., Liu, Y., Li, G.: Path planning for mobile robots based on improved Dijkstra algorithm. J. Tianjin Univ. Urban Constr. 24, 378–381 (2018)
Chen, J., Dong, J., Zhu, X.: Improving the path planning of mobile robots by artificial potential field method. Command Control Simul. 1–6 (2019)
Chen, H., Li, Y., Luo, J.: Research on path planning of mobile robots based on improved A* algorithm optimization. Autom. Instrum. 12, 1–4 (2018)
Shao, W., Luo, Z.: The application of improved visual map method in path planning. J. Nanyang Norm. Univ. 17, 38–42 (2018)
Zhuang, H., Du, S., Wu, T.: Robot path planning and related algorithms research. Sci. Technol. Bull. 3, 210–215 (2004)
Zou, J., Xu, X.: Unmanned vehicle path planning based on improved mixed genetic algorithm. J. Jiamusi Univ. (Nat. Sci. Ed.) 36, 573–577 (2018)
Zhang, Y., Zhang, Z.: Robotic path planning based on improved multi-step ant colony algorithm. Comput. Eng. Des. 39, 3829–3834+3866 (2018)
Zhan, W., Qu, J., Lu, X., Hou, L.: Mobile robot global path planning based on improved ant colony algorithm. Mod. Electron. Technol. 41, 170–173 (2018)
He, Y., Zhang, Z., Han, M., Chen, Q., Huang, X.: Research on ant colony algorithm based on omnidirectional mobile robot path planning. J. Test Technol. 32, 374–380 (2018)
Zhou, J., Zheng, X.: Path planning method based on improved ant colony algorithm. J. Hubei Univ. Technol. 33, 49–52+101 (2018)
Zhu, H., Sun, Z., Woody: Three-dimensional path planning for mobile robots based on improved ant colony algorithm. J. Huazhong Norm. Univ. (Nat. Sci. Ed.) 50, 812–817 (2016)
Wang, X., Yang, L., Zhang, Y., Meng, S.: Robot path planning based on improving potential field ant colony algorithm. Control Decis. Mak. 33, 75–81 (2018)
Acknowledgement
This work was supported by The National Natural Science Foundation of China (Project No. 61662057, 61672301) and Higher Educational Scientific Research Projects of Inner Mongolia Autonomous Region (Project No. NJZC17198).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, T., Song, C., Jiang, J. (2019). Robotic Path Planning Based on Improved Ant Colony Algorithm. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-22796-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22795-1
Online ISBN: 978-3-030-22796-8
eBook Packages: Computer ScienceComputer Science (R0)