基于运动动力学的仿人机器人移动操作位姿优化
Kinodynamics-based Pose Optimization for Humanoid Loco-manipulation
AI总结：
本文提出一种结合运动动力学位姿优化与MPC的仿人机器人推物控制方法，能快速规划最优位姿并协调受力，实现重载推动与强抗扰恢复。
摘要：
本文提出了一种控制仿人机器人推动重物的新方法。该方法结合了基于运动动力学的位姿优化和移动操作模型预测控制（MPC）。所提出的位姿优化考虑了物体-机器人动力学模型、机器人运动学约束和物体参数，为机器人规划最优的推动位姿。移动操作MPC用于通过协调推力和地面反作用力来跟踪最优位姿，确保精确的操作和稳定的运动。数值验证证明了该框架的有效性，使仿人机器人能够在各种参数设置下推动物体。位姿优化可以作为非线性规划（NLP）问题在平均250毫秒内求解。所提出的控制方案允许仿人机器人推动重达20公斤（机器人质量的118%）的物体。此外，它还能从施加0.3秒的120牛顿侧向力扰动中恢复系统。
英文摘要：
This paper presents a novel approach for controlling humanoid robots to push heavy objects. The approach combines kinodynamics-based pose optimization and loco-manipulation model predictive control (MPC). The proposed pose optimization considers the object-robot dynamics model, robot kinematic constraints, and object parameters to plan the optimal pushing pose for the robot. The loco-manipulation MPC is used to track the optimal pose by coordinating pushing and ground reaction forces, ensuring accurate manipulation and stable locomotion. Numerical validation demonstrates the effectiveness of the framework, enabling the humanoid robot to push objects with various parameter setups. The pose optimization can be solved as a nonlinear programming (NLP) problem within an average of 250 ms. The proposed control scheme allows the humanoid robot to push objects weighing up to 20 kg (118$\%$ of the robot's mass). Additionally, it can recover the system from a 120 N lateral force disturbance applied for 0.3 s.
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