Improved Genetic Algorithm in a Static Environment for the Robotic Path Planning Problem
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7820-5_18
Reference15 articles.
1. Kunzli, M., Meier, P., Dornberger, R.: A memory search algorithm for path finding problems compared with a genetic algorithm. In: 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), pp. 66–73. IEEE (2016). https://doi.org/10.1109/ISCBI.2016.7743260
2. Ni, J., Wang, K., Huang, H., Wu, L., Luo, C.: Robot path planning based on an improved genetic algorithm with variable length chromosome. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 145–149. IEEE (2016). https://doi.org/10.1109/FSKD.2016.7603165
3. Persson, S.M., Sharf, I.: Sampling-based A* algorithm for robot path-planning. Int. J. Robot. Res. 33(13), 1683–1708 (2014). https://doi.org/10.1177/0278364914547786
4. Liu, H.: Robot systems for rail transit applications. Elsevier (2020). https://doi.org/10.1016/C2019-0-04615-8
5. Mac, T.T., Copot, C., Tran, D.T., De Keyser, R.: Heuristic approaches in robot path planning: a survey. Robot. Auton. Syst.Auton. Syst. 86, 13–28 (2016)
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