具有实体柔顺性的有腿运动中用于模型预测控制的可变形多体建模
Deformable Multibody Modeling for Model Predictive Control in Legged Locomotion with Embodied Compliance
AI总结：
本文提出一种可变形多体建模方法，通过开发质心复合预测变形惯性（CCPDI）张量并与MPC集成，有效稳定了具有实体柔顺性四足机器人的动态步态，提升了步态稳定性和鲁棒性。
摘要：
本文提出了一种稳定具有实体柔顺性的有腿机器人动态步态的方法。我们的方法引入了刚体和柔顺体的统一描述以近似其变形，并提出了可变形多体系统的公式。我们开发了可变形多体系统的质心复合预测变形惯性（CCPDI）张量，并展示了如何将其与标准实践模型预测控制器（MPC）集成。仿真表明，生成的控制框架能够在相同的MPC配置下，稳定具有刚性和柔顺性脊柱的四足机器人的小跑迈步。与标准MPC相比，开发的基于CCPDI的MPC将地面反作用力分布得更接近身体平衡的启发式方法，因此更有可能稳定柔顺性机器人的步态。参数研究表明，我们的方法在关键参数值的合适范围内保持了一定程度的鲁棒性。
英文摘要：
The paper presents a method to stabilize dynamic gait for a legged robot with embodied compliance. Our approach introduces a unified description for rigid and compliant bodies to approximate their deformation and a formulation for deformable multibody systems. We develop the centroidal composite predictive deformed inertia (CCPDI) tensor of a deformable multibody system and show how to integrate it with the standard-of-practice model predictive controller (MPC). Simulation shows that the resultant control framework can stabilize trot stepping on a quadrupedal robot with both rigid and compliant spines under the same MPC configurations. Compared to standard MPC, the developed CCPDI-enabled MPC distributes the ground reactive forces closer to the heuristics for body balance, and it is thus more likely to stabilize the gaits of the compliant robot. A parametric study shows that our method preserves some level of robustness within a suitable envelope of key parameter values.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
