Date of Award
Open Access Master's Thesis
Master of Science in Electrical and Computer Engineering (MS)
Administrative Home Department
Department of Electrical and Computer Engineering
Committee Member 1
Committee Member 2
One approach to autonomous control of high mobility ground vehicle platforms operating on challenging terrain is with the use of predictive simulation. Using a simulated or virtual world, an autonomous system can optimize use of its control systems by predicting interaction between the vehicle and ground as well as the vehicle actuator state. Such a simulation allows the platform to assess multiple possible scenarios before attempting to execute a path. Physically realistic simulations covering all of these domains are currently computationally expensive, and are unable to provide fast execution times when assessing each individual scenario due to the use of high simulation frequencies (> 1000Hz). This work evaluates using an Unreal Engine 4 vehicle model and virtual environment, leveraging its underlying PhysX library to build a simple unmanned vehicle platform. The simulation is demonstrated to run at low simulation frequencies (< 1000Hz) when performing multiple off road driving maneuvers. Real world path telemetry is used as input to drive the unmanned vehicle's integrated Pure Pursuit and PID autonomous driving control algorithms within the simulation. Cross-track-error and vehicle heading error between the simulation and real world telemetry is then observed after each maneuver's execution. It is concluded after running multiple different vehicle maneuvers in real time at low simulation frequencies, a lower threshold frequency of 190Hz was shown to reliably control the virtual vehicle model with minimal average cross-track-error and heading angle deviation. Higher simulation frequencies approaching 400Hz, the recorded sampling frequency of the real world telemetry for each maneuver, had little change in system performance. Setting the simulation to execute at lower frequencies < 190Hz resulted in a point of exponential increase in both the overall average cross-track-error and heading error. Additional simulation failures were also observed when setting the AV to travel at higher velocities with set simulation frequencies < 190Hz.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Spencer, Matthew Paul, "Off Road Autonomous Vehicle Modeling and Repeatability Using Real World Telemetry via Simulation", Open Access Master's Thesis, Michigan Technological University, 2022.