用于足式机器人接触隐式稳定的混合iLQR模型预测控制
Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots
AI总结：
针对含接触足式机器人MPC实现难的问题，提出HiLQR MPC方法，通过修改代价计算、并行仿真和解析导数计算实现接触序列调整与全身运动规划，在弹跳球和Unitree A1测试中表现优于质心类方法。
摘要：
模型预测控制（MPC）是一种常用的机器人控制策略，但由于混合动力学的复杂特性，含接触的系统难以应用该方法。为在含接触系统上实现MPC，通常会简化动力学模型或固定接触序列的时间安排，以高效规划轨迹。本研究将混合迭代线性二次调节器扩展为MPC架构（HiLQR MPC），具体通过三点实现：1）修改接触模式不匹配时的代价函数计算方式；2）在刚体动力学仿真中利用并行计算；3）采用高效的刚体动力学解析导数计算方法。最终得到的系统可调整参考行为的接触序列，并协调规划全身运动——这在应对大扰动时至关重要。HiLQR MPC在两个系统上进行了测试：首先，在一个简单的驱动弹跳球混合系统上验证了混合代价修改方法的有效性；随后在四足机器人（Unitree A1）上，将HiLQR MPC与采用质心动力学假设的方法进行对比。结果显示，HiLQR MPC在仿真和硬件测试中均优于质心类方法。
英文摘要：
Model Predictive Control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently. In this work, we extend Hybrid iterative Linear Quadratic Regulator to work in a MPC fashion (HiLQR MPC) by 1) modifying how the cost function is computed when contact modes do not align, 2) utilizing parallelizations when simulating rigid body dynamics, and 3) using efficient analytical derivative computations of the rigid body dynamics. The result is a system that can modify the contact sequence of the reference behavior and plan whole body motions cohesively -- which is crucial when dealing with large perturbations. HiLQR MPC is tested on two systems: first, the hybrid cost modification is validated on a simple actuated bouncing ball hybrid system. Then HiLQR MPC is compared against methods that utilize centroidal dynamic assumptions on a quadruped robot (Unitree A1). HiLQR MPC outperforms the centroidal methods in both simulation and hardware tests.
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。
