An Overview of Coal Mine Rescue Robot and its Navigation

Abstract:

After the coal-mine accident, to get information of the coal-mine accident has become the key of disaster rescue and relief, and after disaster, the environment is very bad, the rescue-workers cant arrive at the scene of the accident to obtain information at the first time, so the coal-mine rescue robot was brought into being, it can reach the place where the person hard to go to implement the detection of information and rescue. This paper introduces the research status of coal-mine rescue robot. According to the different methods of navigation, this paper explained the research progress of the methods for robot navigation, and made a summary and prospect for its potential applications and future research work.

• Periodical: Applied Mechanics and Materials (Volumes 365-366)
• Pages: 788-794
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.365-366.788
• Online since: August 2013
• Authors: Yu Xin Qin, Yu Qing Wang, Xiao Jing Chen, Zhi Guo Li
• Keywords: Coal Mine, Multisensor, Navigation, Rescue Robot

References

[1] Zhu Hua．Mine rescue robot research status and the need to focus on the technical issues[J]．Journal of xuzhou engineering institute, 2007, 22 (6): 5-8.

[2] Dong Xiaopo, Wang Xuben. The development of the rescue robot and its application in disaster relief [J]. Disaster prevention and mitigation engineering journal, 2007, 27(1): 112-117.

[3] Li Xuelai, Hu Jingdong. Technology research and application status of coal mine emergency rescue [J]. Coal Engineering, 2005(4): 62-64.

[4] Murphy, R.R., Human-robot interaction in rescue robotics, systems, Man, andCybernetics[C], Part C: Applications and Reviews, IEEE Transactions on, Vol. 34. 2004: 138~153.

DOI: 10.1109/tsmcc.2004.826267

[5] Wang Zheng. Mine detection robot control system is developed and its movement performance analysis[D]. Harbin Institute of Technology, 2007(4): 41-44.

[6] Wnag,L. X Modeling and control of hierarchical systems with fuzzy systems. Automatica. 1997, 33(6): 1041-1053.

[7] Li Weihong, Huo Wei. System modeling of the sliding mode control based on fuzzy logic in the application of the robot. The fifth China robot academic conference proceedings, 1997: 377-382.

[8] Abdi J, Moshiri B, Jafari E, Sedigh AK. Traffic state variables estimating and predicting with neural network via extended Kalman filter algorithm with estimated parameters as offline. In: 8th International symposium on intelligent systems and informatics (SISY), 2010: 383.

DOI: 10.1109/sisy.2010.5647390

[9] Liu F, Meng JE. Design for self-organizing Fuzzy neural network with extended Kalman filter. In: 8th IEEE international conference on control and automation (ICCA), 2010: 423.

DOI: 10.1109/icca.2010.5524416

[10] Majura F, Selekwaa, Damion D, Dunlapb, Dongqing Shib, Emmanuel G. Collins Jr. Robot Navigation in very cluttered environments by preference-based fuzzy behaviors. Robotics and Autonomous Systems, 2008(56) : 231–246.

DOI: 10.1016/j.robot.2007.07.006