Li, K.; Huang, C.; Liang, J.; Zou, Y.; Xu, B.; Yao, Y.; Zhang, Y.; Liu, D. Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field. Sensors2023, 23, 7664.
Li, K.; Huang, C.; Liang, J.; Zou, Y.; Xu, B.; Yao, Y.; Zhang, Y.; Liu, D. Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field. Sensors 2023, 23, 7664.
Li, K.; Huang, C.; Liang, J.; Zou, Y.; Xu, B.; Yao, Y.; Zhang, Y.; Liu, D. Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field. Sensors2023, 23, 7664.
Li, K.; Huang, C.; Liang, J.; Zou, Y.; Xu, B.; Yao, Y.; Zhang, Y.; Liu, D. Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field. Sensors 2023, 23, 7664.
Abstract
High-rise building fires pose a serious threat to the lives and property safety of the people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescue. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of autonomous optimal deployment of base stations in high-rise building fire environment based on UAV group. A novel problem formulation is proposed that solves the non line of sight (NLOS) positioning problem in complex unknown environment. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environment. The NLOS positioning problem in the fire field environment is turned into the line of sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations deployment goal and to avoid collision. Finally, positioning accuracy of our method and that of common method were compared. The simulation result showed that the positioning accuracy of simulated firefighter in the fire field environment was improved greatly.
Keywords
swarm intelligence; trajectroy planning; fire rescue; autonomous deployment; collaborative positioning
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
