Swarm Slam Optimization of the Bionic Working Mode of the Robot
- DOI
- 10.2991/978-94-6463-512-6_11How to use a DOI?
- Keywords
- Swarm Slam; Release Algorithm; Multi-robot Way of Working
- Abstract
How swarm slam robots work in unknown environments is one of the key research topics today. Researchers have found that in the case of optimization of bionic working mode, there is still a research gap on how to obtain more direct and accurate information in an unknown complex environment and how to reduce the working cost of robots. In this study, researchers start with ant colony robots. In terms of bionic working mode, they first adopt the arrangement mode of ant colony incubation to improve the utilization rate of multiple robots to the environment, and then collect and transfer environmental information by means of DCPSLA which solves the problem by means of chain robots. The researchers carried out a series of optimization from the aspects of multi-robot work cost, safety guarantee, work efficiency and so on. Therefore, through consulting papers and collecting knowledge, the research topic of this paper is optimization of the bionic working mode of swarm slam robots.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sheng Yao PY - 2024 DA - 2024/09/23 TI - Swarm Slam Optimization of the Bionic Working Mode of the Robot BT - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024) PB - Atlantis Press SP - 87 EP - 96 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-512-6_11 DO - 10.2991/978-94-6463-512-6_11 ID - Yao2024 ER -