分层架构+模型预测控制，让机器人稳定跨过高低不平地形
Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control
- 分层设计分离离散动作与连续控制，兼顾实时性与最优性
- 在仿真中实现类人机器人跨间隙，比启发式方法更优更快
- 适合需要高动态稳定性的四足/类人机器人运动控制场景
由于这类机器人具有非线性、混合型和高维特性，实时计算稳定的最优控制动作极具挑战。系统混合特性引入了离散与连续变量的耦合，给数值最优控制带来困难。为此，我们提出一种分层架构，将离散变量选择与平滑模型预测控制器（MPC）解耦。该架构结合无梯度采样方法确定离散动作，再以固定离散变量驱动经典平滑MPC。我们在四足机器人跨过间隙和不同高度地形的仿真中验证了效果；在类人机器人上也实现了跨间隙任务。相比常见启发式方法，该方案更具最优性和可靠性，且计算速度优于纯采样方法。
原文摘要 · Abstract (English)
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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