@inproceedings{0bf9e4d86b07437ca89309c6b633e8db,
title = "A bio-inspired self-reparation approach for lattice self-reconfigurable modular robots",
abstract = "The self-reparation of modular self-reconfigurable robots is a fundamental primitive that can be used as part of higher-lever functionality. A bio-inspired approach is proposed for distributed self-reparation, which can lead the robot recover from module fails to the initial configuration. This approach is inspired by the natural growth of plant. The L-systems for describing natural growth is translated to construct robotic structures. Robots reconstruct lost parts by leading other modules to needed positions through the symbol rewriting strategy in L-systems. Simulations and experiments on Seremo robots are provided to verify this method with successful reparation of module fails or removed from the global structure.",
keywords = "Decentralized control, L-systems, Modular robots, Self-reparation",
author = "Dongyang Bie and Yu Zhang and Xingang Zhao and Yanhe Zhu",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",
year = "2019",
doi = "10.1007/978-3-030-27526-6\_58",
language = "英语",
isbn = "9783030275259",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "667--671",
editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Yuwang Liu and Zhaojie Ju and Dalin Zhou",
booktitle = "Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings",
address = "德国",
}