论文标题
BiconMP：一个非线性模型预测控制框架，用于全身运动计划
BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning
论文作者
论文摘要
由于机器人动力学中固有的非线性，腿部机器人的全身动作的在线计划具有挑战性。在这项工作中，我们提出了一个非线性MPC框架，该框架可以通过有效利用机器人动力学的结构来在线生成全身轨迹。 Biconmp用于在一个真正的四倍的机器人上生成各种环状步态，并在不同的地形上评估其性能，对抗不可预见的推动力并在不同步态之间在线过渡。此外，列出了双孔在机器人上产生非平凡的无循环全身动态运动的能力。在模拟中，也使用了相同的方法在人形机器人（TALOS）和另一个四倍的机器人（Anymal）上生成各种动态运动。最后，报告并讨论了对计划范围和频率对非线性MPC框架的影响的广泛经验分析。
Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear MPC framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes and transitioning online between different gaits. Further, the ability of BiConMP to generate non-trivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.
