| dc.description.abstract | Networked embedded systems are valuable tools for guidance and navigation, with applications ranging from UAV swarms to self-driving cars. LASR Lab is working on projects that require networked embedded systems running Kalman filters, such as a smart tower that will send an alert over the Internet if its embedded system detects structural changes in the tower. To demonstrate the architecture that will support these projects, a demonstration has been made consisting of a microcomputer and an Inertial Measurement Unit contained in a football-shaped shell. This football is designed to stream its position and orientation over a network in real time. Like other LASR Lab projects, the football utilizes a Tinker Board microcomputer and a VectorNav IMU.
Data are streamed in real time to a controller laptop over a Wi-Fi connection using the Open MPI protocol. An Extended Kalman Filter and an Unscented Kalman Filter for estimating position, velocity, orientation, and gyroscope biases were developed and implemented in C++, along with calibration routines for estimating initial conditions and noise parameters. Due to a lack of reliable measurements and mathematical bugs, the filters were not successful in estimating position, velocity, or attitude; but the hardware, software, and networking architecture was demonstrated successfully and can be used in future projects. | en |
