利用仿真设计基于GPS感知的野外导航MPC策略
Using simulation to design an MPC policy for field navigation using GPS sensing
AI总结：
本文利用Chrono仿真引擎与ART测试平台开发基于GPS和EKF状态估计的MPC野外导航控制策略，并通过孪生车辆实测验证了Sim2Real的可迁移性。
摘要：
在仿真中建模一个具有基于GPS的精确状态估计能力的鲁棒控制系统，在野外导航应用中是有用的，因为它允许在受控环境中进行测试和验证。这一测试过程将使导航系统能够在仿真中开发和优化，并可直接迁移到现实场景中。多物理场仿真引擎Chrono允许创建在野外难以或危险复现的场景，例如极端天气或地形条件。自主研究测试平台（Autonomy Research Testbed，ART）是一个专门的机器人算法测试平台，与Chrono联合运行，用于开发MPC控制策略以及EKF状态估计器。该平台使用户能够轻松地将自定义算法集成到自主栈中。该模型最初在仿真中开发和使用，然后在现实中的孪生车辆模型上进行测试，以证明仿真与现实之间的可迁移性（也称为Sim2Real）。
英文摘要：
Modeling a robust control system with a precise GPS-based state estimation capability in simulation can be useful in field navigation applications as it allows for testing and validation in a controlled environment. This testing process would enable navigation systems to be developed and optimized in simulation with direct transferability to real-world scenarios. The multi-physics simulation engine Chrono allows for the creation of scenarios that may be difficult or dangerous to replicate in the field, such as extreme weather or terrain conditions. Autonomy Research Testbed (ART), a specialized robotics algorithm testbed, is operated in conjunction with Chrono to develop an MPC control policy as well as an EKF state estimator. This platform enables users to easily integrate custom algorithms in the autonomy stack. This model is initially developed and used in simulation and then tested on a twin vehicle model in reality, to demonstrate the transferability between simulation and reality (also known as Sim2Real).
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。
