Modeling, Reduction, and Control of a Helically Actuated Inertial Soft Robotic Arm via the Koopman Operator
Modeling, Reduction, and Control of a Helically Actuated Inertial Soft Robotic Arm via the Koopman Operator
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发表时间：
2020-11
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通讯作者：
David A. Haggerty;M. Banks;Patrick C. Curtis;Igor Mezi'c;E. Hawkes
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作者：
David A. Haggerty;M. Banks;Patrick C. Curtis;Igor Mezi'c;E. Hawkes
当部署在复杂、微妙和动态的环境中时，软机器人有望提高刚性机器人的安全性和能力。然而，这些系统的无限自由度和高度非线性动力学严重复杂的建模和控制。作为解决这一开放性挑战的一步，我们将数据驱动的Hankel动态模式分解（HDMD）与时间延迟可观的模型识别的高度惯性，螺旋软机械臂与大量的欠驱动自由度。所得到的模型是线性的，因此可以通过线性二次型调节器（LQR）进行控制。使用我们的测试台设备，一个动态的，轻量级的气动织物臂与惯性质量的尖端，我们表明，HDMD和LQR的组合允许我们命令我们的机器人实现任意姿态，只使用开环控制。我们进一步表明，Koopman谱分析给我们一个降维的模式，降低计算复杂性，而不牺牲预测能力的基础。
Soft robots promise improved safety and capability over rigid robots when deployed in complex, delicate, and dynamic environments. However, the infinite degrees of freedom and highly nonlinear dynamics of these systems severely complicate their modeling and control. As a step toward addressing this open challenge, we apply the data-driven, Hankel Dynamic Mode Decomposition (HDMD) with time delay observables to the model identification of a highly inertial, helical soft robotic arm with a high number of underactuated degrees of freedom. The resulting model is linear and hence amenable to control via a Linear Quadratic Regulator (LQR). Using our test bed device, a dynamic, lightweight pneumatic fabric arm with an inertial mass at the tip, we show that the combination of HDMD and LQR allows us to command our robot to achieve arbitrary poses using only open loop control. We further show that Koopman spectral analysis gives us a dimensionally reduced basis of modes which decreases computational complexity without sacrificing predictive power.