SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios
DOI:
https://doi.org/10.54097/hset.v52i.8722Keywords:
Laser SLAM; mobile robot; optimal path planning; autonomous navigation.Abstract
In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.
Downloads
References
Stephen, G. Built in. 12 examples of Rescue Robots you should know. Available from:https://builtin.com/robotics/rescue-robots ,2022. Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.
U.S. Fire Administration, Residential fire estimate summaries. Available from: https://www.usfa.fema.gov/statistics/residential-fires/, 2022.
Wang Jibin. RESAERCH ON THE AUXILIARY RESCUE ROBOT IN NARROW SPACE OF RUINS. Harbin Institute of Technology, 2014.
Duan Xi. Study on Earthquake Rescue Robot Design. Hebei University of Science & Technology, 2019.
Rohmer, E, Ohno, K, Yoshida, T, et al. Integration of a sub-crawlers’ autonomous control in Quince highly mobile rescue robot. In: 2010 IEEE/SICE International Symposium on System Integration.
LI Fu Hao, HOU Shi Ke, BU Chung Guang, et al. Rescue robots for the urban earthquake environment. Disaster Med Public Health Prep. 17(e181), 1–5.
Ito, K, Sugano, S, Iwata, H. Wearable echography robot for trauma patient. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei: IEEE. 2010:4794-4799.
Ito, K, Sugano, S, Iwata, H. Internal bleeding detection algorithm based on determination of organ boundary by low brightness set analysis. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vila Moura: IEEE. 2012:4131-4136
Sebastian B, Williams A, Ben-Tzvi P. Control of a head stabilization system for use in robotic disaster response. In: ASME 2017 International Mechanical Engineering Congress and Exposition. Tampa, Florida. USA: ASME. 2017.
LI Xu, FU Xiang, DUAN Bin. Forest Fire Identification in Complex Background Based on Images Generation and Feature Fusion. Computer Integrated Manufacturing Systems, 2022, 39(04): 465-472.
LI Xiao Na. Stay away from winter fire hazards. China Fire, 2022, 564(11): 33-37.
ZHAO Yi Mao, OUYANG Jia Tai, WANG Guan Lin, et al. Design of Autonomous Navigation System for Indoor Mobile Robot Based on ROS. Journal of Heilongjiang University of Technology (Comprehensive Edition), 2022, 22(11): 82-91.
GAO Xi Qiang. Research on Motion Trajectory Planning of Mobile Robot Based on TEB Algorithm. Agricultural equipment & vehicle engineering, 2022, 60(07): 126-129.
SONG Huai Bo, DUAN Yuan Chao, LI Rong, et al. Autonomous Navigation System for Pasture Intelligent Overthrowing Grass Robot Based on Laser SLAM. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(02): 293-301.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







