基于分层架构和模型预测控制的受限支撑面移动
Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control
AI总结：
针对腿式机器人混合特性导致的实时最优控制难题，提出分层架构分离离散变量选择与平滑MPC，结合无梯度与梯度方法，在四足和仿人机器人仿真中验证其比启发式方法更优可靠、比纯采样方法更快。
摘要：
由于腿式机器人具有非线性、混合性和高维度的特性，实时计算稳定且最优的移动控制动作十分困难。系统的混合特性引入了离散变量与连续变量的组合，这给数值最优控制带来了问题。为应对这些挑战，我们提出一种分层架构，将离散变量的选择与平滑模型预测控制器（MPC）分离。该分层形式通过结合无梯度和基于梯度的方法，在不牺牲实时性能的前提下实现了在线灵活性与最优性。该架构利用基于采样的方法确定离散变量，并采用使用这些固定离散变量的经典平滑MPC形式。我们在四足机器人跨越间隙和踏上不同高度地形的场景中展示了结果；在仿真中，我们在人形机器人上演示了该控制器用于间隙穿越。结果表明，这种分层方法比常见的基于启发式的方法更优、更可靠，且计算速度快于纯采样方法。
英文摘要：
Computing stabilizing and optimal control actions for legged locomotion in real time is difficult due to the nonlinear, hybrid, and high dimensional nature of these robots. The hybrid nature of the system introduces a combination of discrete and continuous variables which causes issues for numerical optimal control. To address these challenges, we propose a layered architecture that separates the choice of discrete variables and a smooth Model Predictive Controller (MPC). The layered formulation allows for online flexibility and optimality without sacrificing real-time performance through a combination of gradient-free and gradient-based methods. The architecture leverages a sampling-based method for determining discrete variables, and a classical smooth MPC formulation using these fixed discrete variables. We demonstrate the results on a quadrupedal robot stepping over gaps and onto terrain with varying heights. In simulation, we demonstrate the controller on a humanoid robot for gap traversal. The layered approach is shown to be more optimal and reliable than common heuristic-based approaches and faster to compute than pure sampling methods.
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