An Optimization-based Approach to Controlling Agile Motions for a Quadruped Robot
An Optimization-based Approach to Controlling Agile Motions for a Quadruped Robot
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基于优化的四足机器人敏捷运动控制方法
DOI：
10.3929/ethz-a-010644954
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
R. Siegwart
中科院分区：
文献类型：
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作者：
Christian Gehring;Stelian Coros;Marco Hutter;C. D. Bellicoso;Huub Heijnen;Remo Diethelm;Michael Bloesch;Péter Fankhauser;Jemin Hwangbo;A. M. Hoepflinger;R. Siegwart
本研究以参数化、模型化、状态回授控制器来控制四足机器人的跑跳动作。受自然界中观察到的运动学习原理的启发，我们的方法通过重复执行相同运动任务的轻微变化来自动微调控制器的参数。这种通过实践学习的过程是在模拟中进行的，以便最好地利用计算资源，并防止机器人损坏自己。为了确保仿真结果与硬件平台的行为充分匹配，我们引入并验证了柔顺致动系统的精确模型。所提出的方法进行了实验验证的扭矩可控四足机器人StarlETH通过执行蹲跳和动态步态，如跑步小跑，pronk和跳跃步态。
This work approaches the problem of controlling quadrupedal running and jumping motions with a parametrized, model-based, state-feedback controller. Inspired by the motor learning principles observed in nature, our method automatically fine tunes the parameters of our controller by repeatedly executing slight variations of the same motion task. This learn-through-practice process is performed in simulation in order to best exploit computational resources and to prevent the robot from damaging itself. In order to ensure that the simulation results match the behavior of the hardware platform sufficiently well, we introduce and validate an accurate model of the compliant actuation system. The proposed method is experimentally verified on the torque-controllable quadruped robot StarlETH by executing squat jumps and dynamic gaits such as a running trot, pronk and a bounding gait.