Accurate Position Estimation of Mobile Robot Based on Cyber-Physical-Social Systems (CPSS)

When planning the trajectory of a mobile robot, it is usually necessary to use sensors to collect a large amount of position information. Because traditional computing methods cannot effectively use huge data sources, an edge computing that implements edge intelligent services on the side of network edge near the data source is proposed, which speeds up the process of data processing. However, the data collected through sensors may contain gross errors. In general, the influence of gross errors on state estimation are rarely considered when using particle filter algorithms for state estimation. In fact, the measurements of dynamic systems are often affected by different types of gross errors in the actual application process. Therefore, it is a problem worth studying that how to detect and compensate for different types of gross errors. In this paper, an improved particle filter algorithm is proposed for the position estimation of mobile robot dynamic system. Firstly, the gross error identification method is used to identify the types of gross errors, and then the various gross errors are compensated. Finally, the particle filter algorithm based on the measurements compensation is obtained. Simulation experiments on the position estimation of mobile robots are carried out to verify the effectiveness of the proposed method in solving the measurements with gross errors. The precise position estimation of the mobile robot is achieved. Through the simulation experiments on the position estimation problem of mobile robots, it is verified that the proposed method is effective in solving the measurements with gross errors. And the accurate position estimation of the mobile robot is realized.