Development of an Embedded IoT Board for Real-Time Floor Estimation of Autonomous Robots
As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward compatibility with older buildings, and increased points of failure in the system architecture. This thesis presents the design and evaluation of a non-invasive IoT board for floor-level estimation that requires no modification to existing elevator systems. Developed in collaboration with Rocky Mountain Robotech LLC, the device is intended to assist service robots by providing floor-awareness using barometric pressure sensing. The system operates in two primary modes: a training mode, where it identifies characteristic pressure changes between building floors, and a normal operation mode, where it references this data to estimate floor position in real-time. Testing was conducted in buildings between two and four stories tall in Dallas, Texas, and Denver, Colorado. During training mode, the device correctly queued incoming pressure data and applied both a moving average filter and the Ramer-Douglas-Peucker (RDP) algorithm to isolate plateaus corresponding to distinct floor levels. After training, the board reliably transitioned to normal operation mode, continuing to collect and compare pressure data to stored floor values. Bluetooth communication with a tablet on the robot enabled the transmission of commands to initiate training and other actions, while data stored in non-volatile memory was preserved across power cycles. These results confirm that the system can distinguish between floors and maintain robust communication without requiring elevator integration. It demonstrates a low-cost, modular approach to floor estimation that avoids common barriers to adoption, such as infrastructure modification or reliance on high-precision sensors. However, while initial testing validates core functionality, further testing is needed to assess long-term reliability, sensitivity to weather and environmental changes, and generalizability across a wider variety of building types and layouts. This work shows that thoughtfully designed, non-invasive IoT hardware can meet key needs in service robotics—enhancing autonomy and safety without compromising existing infrastructure.