Abstract
An improved ant-type approach, ant colony optimization (ACO) model, integrated with a heading direction scheme (HDS) to real-time collision-free navigation and mapping of an autonomous robot is proposed in this paper. The developed HDS-based ACO model for concurrent map building and safety-aware navigation is capable of remedying the shortcoming of risky distance from obstacles in combination with the Dynamic Window Approach (DWA) algorithm as a local navigator. Its effectiveness and efficiency of the developed real-time hybrid map building and safety-aware navigation of an autonomous robot have been successfully validated by simulated experiments and comparison studies.
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References
Chia, S.H., Su, K.L., Guo, J.H., Chung, C.Y.: Ant colony system based mobile robot path planning. In: Proceedings of 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp. 210–213 (2010)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. The MIT Press, San Francisco (2004)
Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)
Garcia, M.A.P., Montiel, O., Castillo, O., Lveda, R.S., Melin, P.: Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl. Soft Comput. 9(1), 1102–1110 (2009)
Luo, C., Mo, H., Shen, F., Zhao, W.: Multi-goal motion planning of an autonomous robot in unknown environments by an ant colony optimization approach. In: Proceeings of the Sixth International Conference on Swarm Intelligence, pp. 519–527 (2016)
Luo, C., Yang, S.X.: A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments. IEEE Trans. Neural Netw. 19(7), 1279–1298 (2008)
Mo, H., Luo, C., Liu, K.: Robot indoor navigation based on computer vision and machine learning. In: Proceedings of the Sixth International Conference on Swarm Intelligence, pp. 528–534 (2016)
Vasak, J., Hvizdos, J.: Vehicle navigation by fuzzy cognitive maps using sonar and RFID technologies. In: Proceedings of IEEE 14th International Symposium on Applied Machine Intelligence and Informatics, pp. 75–80 (2016)
Yang, S.X., Luo, C.: A neural network approach to complete coverage path planning. IEEE Trans. Syst. Man Cybern. 34(1), 718–725 (2004)
Yi, X., Zhu, A., Yang, S.X., Luo, C.: A bio-inspired approach to task assignment of swarm robots in 3-D dynamic environments. IEEE Trans. Cybern. 47(4), 974–983 (2017)
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Luo, C., Shen, F., Mo, H., Chu, Z. (2017). An Improved Ant-Driven Approach to Navigation and Map Building. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_33
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DOI: https://doi.org/10.1007/978-3-319-61824-1_33
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