To improve the accuracy of typhoon prediction, it is necessary to detect the internal structure of a typhoon. The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality. This study proposes a floating weather sensing node motion modeling method based on the chaotic mapping. After the chaotic attractor is obtained by simulation, the position trajectory of the floating weather sensing node is obtained by space and coordinate conversion, and the three-dimensional velocity of each point on the position trajectory is obtained by multidimensional linear interpolation. On this basis, the established motion model is used to study the Doppler frequency shift, which is based on the software and physical platform. The software simulates the relative motion of the transceiver and calculates the Doppler frequency shift. The physical platform can add the Doppler frequency shift to the actual transmitted signal. The results show that this method can effectively reflect the influence of the floating weather sensing node motion on signal transmission. It is helpful to research the characteristics of the communication link and the design of a signal transceiver for typhoon detection to further improve the communication quality and to obtain more accurate interior structure characteristic data of a typhoon.

For a multi-robot system, the accurate global map building based on a local map obtained by a single robot is an essential issue. The map building process is always divided into three stages: single-robot map acquisition, multi-robot map transmission, and multi-robot map merging. Based on the different stages of map building, this paper proposes a multi-stage optimization (MSO) method to improve the accuracy of the global map. In the map acquisition stage, we windowed the map based on the position of the robot to obtain the local map. Furthermore, we adopted the extended Kalman filter (EKF) to improve the positioning accuracy, thereby enhancing the accuracy of the map acquisition by the single robot. In the map transmission stage, considering the robustness of the multi-robot system in the real environment, we designed a dynamic self-organized communication topology (DSCT) based on the master and slave sketch to ensure the efficiency and accuracy of map transferring. In the map merging stage, multi-layer information filtering (MLIF) was investigated to increase the accuracy of the global map. We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods. In addition, the practicability of this method has been verified on the Turtlebot3 burger robot. Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.