Vehicle autonomy requires accurate modeling of both kinematic and dynamic parameters of a given craft. While many of these parameters may be measured directly or determined through experimentation, it is often desirable to measure and update them in the field, that is, while the vehicle is operating. The challenge with online system identification, however, is the lack of an absolute reference to determine the true value of a given variable of interest. When used appropriately, global navigation satellite system (GNSS) sensors are able to provide such a reference for these variables. Furthermore, these sensors can operate at a high enough frequency to provide near real-time feedback to the vehicle. A related challenge for autonomous vehicles is sensor calibration. Many inexpensive inertial measurement units (IMU) are subject to drift or error due to changes in the environment. For these challenges the GNSS sensor may also provide a useful reference to determine sensor drift as well as a means for recalibration. In this work we present methods for addressing several challenges relevant to autonomous ground vehicles using a GNSS sensor as an absolute reference. We have built a small scale autonomous ground vehicle (AGV) as a test platform equipped with onboard odometry, an IMU and a commercial GNSS sensor. We initially demonstrate methods using GNSS data to determine parameters of the dynamic and kinematic models. We determine the coefficients of a frequency domain model of the AGV drivetrain using on a simple DC motor and an autoregresson with external inputs (ARX) system identification technique. We extend this model to determine the longitudinal transfer function of the system using step function tests and GNSS-derived velocity measurements. We also demonstrate methods to determine the AGV kinematic model parameters: turning radius, wheelbase, effective tire radius of the AGV, and steering mechanism parameters. We compare lab measurements and against results obtained with the GNSS sensor using least-squares fitting methods. Using the GNSS-derived kinematic parameters we determine the best estimate of the AGV odometry model in comparison to the absolute position and heading as determined by the GNSS sensor. Lastly, we demonstrate methods to calibrate two onboard sensors. The first method collects magnetometer readings in the local tangent plane and computes calibration factors using an iterative least squares fit of the data to a unit circle. The second method calibrates the yaw rate as measured by the $z$- axis gyroscope sensor using the GNSS-derived rate of change of heading angle.

GNSS-Assisted System Identification of Autonomous Ground Vehicle Model and Sensor Parameters

Vehicle autonomy requires accurate modeling of both kinematic and dynamic parameters of a given craft. While many of these parameters may be measured directly or determined through experimentation, it is often desirable to measure and update them in the field, that is, while the vehicle is operating. The challenge with online system identification, however, is the lack of an absolute reference to determine the true value of a given variable of interest. When used appropriately, global navigation satellite system (GNSS) sensors are able to provide such a reference for these variables. Furthermore, these sensors can operate at a high enough frequency to provide near real-time feedback to the vehicle. A related challenge for autonomous vehicles is sensor calibration. Many inexpensive inertial measurement units (IMU) are subject to drift or error due to changes in the environment. For these challenges the GNSS sensor may also provide a useful reference to determine sensor drift as well as a means for recalibration. In this work we present methods for addressing several challenges relevant to autonomous ground vehicles using a GNSS sensor as an absolute reference. We have built a small scale autonomous ground vehicle (AGV) as a test platform equipped with onboard odometry, an IMU and a commercial GNSS sensor. We initially demonstrate methods using GNSS data to determine parameters of the dynamic and kinematic models. We determine the coefficients of a frequency domain model of the AGV drivetrain using on a simple DC motor and an autoregresson with external inputs (ARX) system identification technique. We extend this model to determine the longitudinal transfer function of the system using step function tests and GNSS-derived velocity measurements. We also demonstrate methods to determine the AGV kinematic model parameters: turning radius, wheelbase, effective tire radius of the AGV, and steering mechanism parameters. We compare lab measurements and against results obtained with the GNSS sensor using least-squares fitting methods. Using the GNSS-derived kinematic parameters we determine the best estimate of the AGV odometry model in comparison to the absolute position and heading as determined by the GNSS sensor. Lastly, we demonstrate methods to calibrate two onboard sensors. The first method collects magnetometer readings in the local tangent plane and computes calibration factors using an iterative least squares fit of the data to a unit circle. The second method calibrates the yaw rate as measured by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$z$</tex> - axis gyroscope sensor using the GNSS-derived rate of change of heading angle. 

Towards Digital Twin Architectures Based on Dockerized Private Clouds: A Use Case of Automated Guided Vehicles

The use of autonomous systems is burgeoning in the world today for many applications in many fields from scientific, industrial, to military. At the same time, advances in semiconductor technology have enabled ever smaller, complex and dedicated microprocessors. This work details a control system architecture that takes advantage of these advances for use in resource-constrained autonomous systems. The architecture consists of a real time hardware controller, a guidance and navigation computer, and an edge TPU for machine learning inferences. While the latter two processors are commercially available, a dedicated, modular real time controller is not. Therefore we present an open source design for a real time controller that is intended to be adapted to many types of autonomous systems. We present three different vehicle platforms that implement this control system including a ground vehicle, a surface vessel, and a quadcopter. Finally, we present results using this control architecture on a few key topics of interest in autonomous systems. The first is a novel spatial estimation algorithm called partitioned ordinary kriging that is designed for resource-constrained systems and can be used for path finding during mapping missions. The second result pertains to a sensor calibration utilizing sensor data collected from the real time controller and performed on the navigation computer. Finally, we demonstrate using the edge TPU (tensor processing unit) for object detection using an onboard camera and an object detection algorithm using machine learning.

A Distributed Control Architecture for Resource-constrained Autonomous Systems

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A Distributed Control Architecture for Resource-constrained Autonomous Systems