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Recently, Autonomous Ground Vehicles (AGV) and mobile robots have been rapidly developed in various engineering applications, such as Industry 4.0 factory and smart manufacturing. Indoor navigation was one of the most important tasks for the mobile systems as they were often designed to move from one location to another location autonomously without contacting the surrounding objects along the moving path in a usually dynamic and complex indoor environment. There were two key steps to achieve Simultaneous Localization and Mapping (SLAM). First, indoor positioning of the mobile system based on some measurements was done. The second step was to navigate itself inside the indoor map. This was a very challenging problem because there always existed uncertainties in the measurements. It was desired to estimate the positioning errors and determine a safe moving path with high reliability. This paper presented the methodologies for wireless indoor positioning and navigation of AGV with measurement uncertainties. Two kinds of AGV moving trajectories with various design parameters were simulated: a linear trajectory and a curved one. It was found that both greater number of sensors being used for wireless measurements and greater number of measurement trials for multilateration could effectively improve the accuracy of AGV positioning.

Probabilistic Indoor Positioning and Navigation (PIPN) of Autonomous Ground Vehicle (AGV) Based on Wireless Measurements