论文标题
通过kino-Dynamic MPC与混合系统DDP通过kino-Dynamic MPC合成多功能实时运动
Versatile Real-Time Motion Synthesis via Kino-Dynamic MPC with Hybrid-Systems DDP
论文作者
论文摘要
通常通过求解轨迹优化问题并使用跟踪控制器来执行轨迹，通常可以在四倍的机器人上实现诸如跳跃之类的专业动议。这种方法与模型预测控制（MPC）策略平行，该策略通常通过在线重新计划控制常规步态。在这项工作中，我们提出了一种非线性MPC（NMPC）技术，该技术可以在统一的框架内解锁专门运动技能和常规运动的即时重新计划。 NMPC的原因是混合动力学模型，并使用约束差分动态编程（DDP）求解器的变体来解决。拟议的NMPC使机器人能够发挥各种敏捷技能，例如跳跃，界限和小跑，以及这些技能之间的快速过渡。我们通过三个具有挑战性的运动序列评估了提出的算法，这些运动序列结合了两个四倍的平台，即Unitree A1和MIT Mini Cheetah，显示了其有效性和一般性。
Specialized motions such as jumping are often achieved on quadruped robots by solving a trajectory optimization problem once and executing the trajectory using a tracking controller. This approach is in parallel with Model Predictive Control (MPC) strategies that commonly control regular gaits via online re-planning. In this work, we present a nonlinear MPC (NMPC) technique that unlocks on-the-fly re-planning of specialized motion skills and regular locomotion within a unified framework. The NMPC reasons about a hybrid kinodynamic model, and is solved using a variant of a constrained Differential Dynamic Programming (DDP) solver. The proposed NMPC enables the robot to perform a variety of agile skills like jumping, bounding, and trotting, and the rapid transition between these skills. We evaluated the proposed algorithm with three challenging motion sequences that combine multiple agile skills, on two quadruped platforms, Unitree A1, and MIT Mini Cheetah, showing its effectiveness and generality.
