Fire Recognition and Path Planning for Fire Fighting Robots Based on Machine Vision Slam Technology
- DOI
- 10.2991/978-94-6463-512-6_13How to use a DOI?
- Keywords
- Machine vision; Machine learning; SLAM; Fire robot
- Abstract
Firefighting robots, as an important part of firefighting, make great contributions to the life safety and economic security of the society. Among the firefighting robots, the firefighting robots utilizing machine vision slam technology are the most promising. This paper describes the main algorithms of today’s firefighting robots based on machine vision slam technology. The fire recognition technology mainly utilizes the You Only Look Once(YOLO) algorithm, with YOLOv5 and YOLOv8 as the main algorithms. The main algorithms for path planning techniques are A-star(A*) algorithm and Ant Colony Optimization respectively. For these main algorithms, the article evaluates their advantages and disadvantages, and proposes directions that can be improved. Then, the article introduces two feasible solutions based on transformer model for visual domain and visual Simultaneous Localization and Mapping(SLAM) domain respectively, which have been verified with good results in other domains. Finally, this paper is summarized to evaluate the algorithms and algorithm optimization that have been studied.
- 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 - Lequan Li PY - 2024 DA - 2024/09/23 TI - Fire Recognition and Path Planning for Fire Fighting Robots Based on Machine Vision Slam Technology BT - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024) PB - Atlantis Press SP - 106 EP - 114 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-512-6_13 DO - 10.2991/978-94-6463-512-6_13 ID - Li2024 ER -