This paper proposes a novel orientation-aware model predictive control (MPC) for dynamic humanoid walking that can plan footstep locations online. Instead of a point-mass model, this work uses the augmented single rigid body model (aSRBM) to enable the MPC to leverage orientation dynamics and stepping strategy within a unified optimization framework. With the footstep location as part of the decision variables in the aSRBM, the MPC can reason about stepping within the kinematic constraints. A task-space controller (TSC) tracks the body pose and swing leg references output from the MPC, while exploiting the full-order dynamics of the humanoid. The proposed control framework is suitable for real-time applications since both MPC and TSC are formulated as quadratic programs. Simulation investigations show that the orientation-aware MPC-based framework is more robust against external torque disturbance compared to state-of-the-art controllers using the point mass model, especially when the torso undergoes large angular excursion. The same control framework can also enable the MIT Humanoid to overcome uneven terrains, such as traversing a wave field.
翻译:本文建议为动态人类行走提供一种新的定向认知模型预测控制(MPC),该模型可以在线规划脚步位置。 这项工作不使用点质量模型,而是使用强化的单一硬体模型(aSRBM),使MPC能够在统一优化框架内利用定向动态和踏脚战略。 以步位位置作为SRBM中决定变量的一部分, MPC可以合理地在运动力限制范围内踏足。 任务空间控制器(TSC)跟踪MPC 的体容和摇摆腿参考参考输出,同时利用人体全序动态。 拟议的控制框架适合实时应用, 因为 MPC 和 TSC 都作为二次程序。 模拟调查显示, 定位MPC 框架比使用点质量模型的状态控制器更有力, 特别是当托尔索发生大型角外向时, 同样的控制框架还可以使麻省人类机克服不均匀的地形, 如跨波场 。