Abstract
Snake-like robots have been widely studied and developed to exploit their flexible mobility and versatility. However, when encoutering powerful damages, how to recover the functionality is seldom investigated. This paper proposed a trial-and-error learning approach for damage recovery for 3-dimensional snake-like robots. The proposed method can guide snake-like robots to find compensation behavior in the absence of the pre-specified damage models. Our proposed method is evaluated by experiments in real world and various simulations.
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CITATION STYLE
Guan, Z., Huang, J., Jian, Z., liu, L., Cheng, L., & Huang, K. (2018). A Learning Based Recovery for Damaged Snake-Like Robots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11307 LNCS, pp. 26–39). Springer Verlag. https://doi.org/10.1007/978-3-030-04239-4_3
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