Directional RSS-Based Localization for Multi-Robot Applications

This paper presents new techniques for simultaneous localization of multiple mobile robots using directional received signal strength (RSS). The RSS data is collected from the XBee wireless module with ZigBee technology. Directional RSS was achieved by a corner reflector antenna installed on the top of each mobile robot. The design parameters of the antenna were chosen by experiments and physical constraints of the robots. To map the RSS data to physical distance values, we first filtered the data using the well-known RANdom Sample Consensus (RANSAC) technique and then applied the least square regression method to further remove a large amount of outliers. In addition, real-time multi-robot localization and path planning are achieved by using an online statistical filter. The algorithm first identifies well-conditioned RSS values and sets a dynamic step size for trajectory generation. The online statistical filter was experimentally evaluated by comparing with two other algorithms, i.e., the Kalman and particle filters, and showed better performance than the other filters in terms of processing time. Preliminary evaluation on the directivity achieved by the corner reflector revealed that the mean orientation error of −4.01◦. Key–Words: Received signal strength (RSS), random sample consensus (RANSAC), multi-robot localization, antenna design, mobile robots