Abstract
Ant colony algorithm can be used for the automatic path planning of complex terrain. However, most of the current ant colony algorithms are based on 2D terrain, without considering the influence of terrain slope on path selection. In addition, the parallelism of the algorithm is not used, which makes the algorithm time-consuming. Aiming at the above problems, this paper proposes an improved ant colony algorithm 3D-PACA. First of all, we raster the map using bilinear interpolation method and translate the 3D terrain into 2D terrain according to the given slope threshold. And then we combine OpenMP parallel programming technology to accelerate this algorithm by mining the concurrency of ant colony algorithm using the idea of parallel computing. The simulation results show that compared with the traditional ant colony algorithm, the improved algorithm can effectively adapt to the three-dimensional terrain, and can get a speedup of about 3 times.
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
Liu, C., Chang, J., Liu, C.: Path planning for mobile robot based on an improved probabilistic roadmap method. Chin. J. Electron. 18(3), 395–399 (2009)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 26(1), 29–41 (1996). doi:10.1109/3477.484436
Stutzle, T., Hoos, H.: MAX-MIN ant system and local search for the traveling salesman problem. In: IEEE International Conference on Evolutionary Computation, pp. 309–314. IEEE (1997). doi:10.1109/ICEC.1997.592327
Garcia, M.A.P., Montiel, O., Castillo, O., et al.: Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Applied Soft Comput. 9(3), 1102–1110 (2009). doi:10.1016/j.asoc.2009.02.014
Botee, H.M., Bonabeau, E.: Evolving ant colony optimization. Adv. Complex Syst. 1(02n03), 149–159 (1998). doi:10.1142/S0219525998000119
Liu, C.A., Yan, X.H., Liu, C.Y., et al.: Dynamic path planning for mobile robot based on improved ant colony optimization algorithm. Acta Electron. Sin. 5, 042 (2011)
Pan, J., Wang, X.S., Cheng, Y.H.: Improved ant colony algorithm for mobile robot path planning. Zhongguo Kuangye Daxue Xuebao (J. China Univ. Min. Technol.) 41(1), 108–113 (2012)
Lim, K.K., Ong, Y.S., Lim, M.H., et al.: Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput. 12(10), 981–994 (2008). doi:10.1007/s00500-007-0264-x
Xing, H., Pan, W., Zou, X.: A novel improved quantum genetic algorithm for combinatorial optimization problems. Acta Electron. Sin. 35(10), 1999 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, M., Jiang, Z., Wang, L., Yao, Y. (2017). Research on Parallel Ant Colony Algorithm for 3D Terrain Path Planning. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 751. Springer, Singapore. https://doi.org/10.1007/978-981-10-6463-0_7
Download citation
DOI: https://doi.org/10.1007/978-981-10-6463-0_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6462-3
Online ISBN: 978-981-10-6463-0
eBook Packages: Computer ScienceComputer Science (R0)