关于在合成自主地面机器人路径跟随控制策略中使用仿真的一项研究
A Study on the Use of Simulation in Synthesizing Path-Following Control Policies for Autonomous Ground Robots
AI总结：
研究使用仿真器为自主地面机器人建立路径跟随控制策略的有效性，提出基于数字孪生生成PID、MPC及两个NN控制策略，并通过随机微仿真测试排名验证仿真与现实结果的一致性。
摘要：
我们报告了在回答以下问题时获得的结果与见解：使用仿真器为自主地面机器人建立路径跟随控制策略的有效性如何？虽然仿真器的质量决定了该问题的答案，但我们发现对于本文使用的仿真平台，一旦受控机器人的数字孪生可用，为路径规划生成四种控制策略就变得简单直接。在仿真中建立并随后在现实世界中演示的控制策略包括PID控制、MPC以及两个基于神经网络（NN）的控制器。通过模仿学习训练这两个NN控制器，使用七个简单机动动作即可快速完成：顺时针跟随三个圆圈、逆时针跟随相同的圆圈以及直线行驶。采用随机微仿真的测试随机化过程用于对四种控制策略的“优良度”进行排名。在仿真中观察到的策略排名与在现实世界中测试控制策略时观察到的排名高度相关。所使用的仿真平台是公开可用的，并以BSD3许可证作为开源发布；提供公共Docker镜像用于可复现性研究。它包含一个动力学引擎、一个传感器仿真器、一个ROS2桥接，以及一个ROS2自主栈，后者在仿真和现实世界实验中均被采用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
