BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning
BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning
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DOI：
10.1109/tro.2022.3228390
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发表时间：
2023-01-06
影响因子：
7.8
通讯作者：
Righetti, Ludovic
中科院分区：
文献类型：
--
作者：
Meduri, Avadesh;Shah, Paarth;Righetti, Ludovic
腿式机器人的全身运动的在线规划是具有挑战性的，由于在机器人动力学固有的非线性。在这项工作中，我们提出了一个非线性模型预测控制（MPC）框架，BiConMP，它可以有效地利用机器人动力学的结构在线生成全身轨迹。BiConMP用于在真实的四足机器人上生成各种循环步态，并在不同的地形上评估其性能，对抗不可预见的推力，并在不同的步态之间在线过渡。此外，能力的BiConMP产生非平凡的非循环的全身动态运动的机器人。同样的方法也被用来产生各种动态运动在MPC上的人形机器人（Talos）和另一个四足机器人（AnYmal）的仿真。最后，一个广泛的实证分析的影响，规划水平和频率的非线性MPC框架的报告和讨论。
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 model predictive control (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. Furthermore, the ability of BiConMP to generate nontrivial 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.