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.

翻译：我们报告了在回答以下问题时获得的结果和见解：使用仿真器为自主地面机器人建立路径跟踪控制策略的效果如何？虽然仿真器的质量决定了这一问题的答案，但我们发现在本文使用的仿真平台上，一旦拥有受控机器人的数字孪生模型，生成四种路径规划控制策略便十分直接。在仿真中建立并随后在现实世界中验证的控制策略包括：PID控制、模型预测控制（MPC）以及两种基于神经网络（NN）的控制器。通过模仿学习训练这两种神经网络控制器得以快速完成，仅使用了七种简单操作：顺时针跟随三个圆、逆时针跟随相同三个圆以及直线行驶。一种采用随机微仿真的测试随机化过程被用于对这四种控制策略的“优劣”进行排序。仿真中记录的策略排序与在现实世界中测试控制策略时观察到的排序高度一致。所使用的仿真平台已公开，并以BSD3许可开源发布；此外还提供了公共Docker镜像以供可重复性研究。该平台包含动力学引擎、传感器仿真器、ROS2桥接器以及ROS2自主软件栈，后者同时在仿真和现实世界实验中使用。