  We report results obtained and insights gained while answering the following
question: how effective is it to use a simulator to establish path following
control policies for an autonomous ground robot? While the quality of the
simulator conditions the answer to this question, we found that for the
simulation platform used herein, producing four control policies for path
planning was straightforward once a digital twin of the controlled robot was
available. The control policies established in simulation and subsequently
demonstrated in the real world are PID control, MPC, and two neural network
(NN) based controllers. Training the two NN controllers via imitation learning
was accomplished expeditiously using seven simple maneuvers: follow three
circles clockwise, follow the same circles counter-clockwise, and drive
straight. A test randomization process that employs random micro-simulations is
used to rank the ``goodness'' of the four control policies. The policy ranking
noted in simulation correlates well with the ranking observed when the control
policies were tested in the real world. The simulation platform used is
publicly available and BSD3-released as open source; a public Docker image is
available for reproducibility studies. It contains a dynamics engine, a sensor
simulator, a ROS2 bridge, and a ROS2 autonomy stack the latter employed both in
the simulator and the real world experiments.
