In this paper, two large-scale fading path loss models are presented based on indoor and outdoor channel measurements at 73 GHz. The line-of-sight millimeter-wave propagation measurement campaigns were uniquely conducted within the indoor and outdoor environments at an airport setting, i.e., the Boise Airport. The channel measurements were made with directional transmit and receive antennas with a 24 dBi gain at different receive antenna heights. From the measured data, we obtained the parameters of two path loss models, i.e., the close-in reference distance model (CIM) and the floating-intercept model (FIM). Results show that the path loss exponents estimated from the CIM are very close to that of the free-space path loss model, while the FIM provides a better fit to the measurement data.

The use of robots is becoming more common in society. Industrial robots are being developed to work with people, and lower-force collaborative robots are being developed to help people in their everyday lives. These may need fast and sophisticated motion control and behavioral algorithms, but are expected to be more compact and lower cost. This paper proposes a processor plus FPGA solution for the control systems for such robots, where the FPGA performs all real-time tasks, freeing the processor to run lower-frequency high level control and interface to other devices such as camera systems. A demonstrator robot is designed, combining multi-axis motion control with 3D robot vision

Localization in indoor environments is essential to further support automation in a wide array of scenarios. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate wireless localization in indoor environments, however, has proved a challenging task due to multi-path fading. Additionally, due to the effects of multi-path fading, methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with that of odometry sensors to track the location of a mobile robot. This paper presents significant real-world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to reduce the localization error for a mobile robot.