基于神经动力学辨识的多旋翼飞行器软控制
Soft Multicopter Control using Neural Dynamics Identification
摘要：
柔性体机器人的动态控制，以提供低维驱动输入的复杂行为是具有挑战性的。在本文中，我们提出了一种计算方法来自动生成通用的，欠驱动控制策略，驱动具有复杂结构和非线性动力学的软体机器。我们的目标应用专注于软多旋翼的自主控制，其特点是弹性材料组件，非传统形状和非对称转子布局，以精确地提供柔性变形和敏捷运动。我们的方法的核心部分在于一个轻量级的神经代理模型来识别和预测一组表征弹性软体的几何变量的时间演化。这种基于物理的学习模型被进一步集成到一个线性二次调节器（LQR）控制回路中，该回路通过一种新的在线定点再线性化方案来增强，以适应动态身体平衡，从而大大减少了由传统的全尺度传感-仿真-控制工作流程所引起的计算开销。我们通过为广泛的定制软多旋翼设计生成控制器并在高保真物理仿真环境中对其进行测试来证明我们的方法的有效性。控制算法使多旋翼飞行器能够执行各种任务，包括悬停、轨迹跟踪、巡航和主动变形。
英文摘要：
Dynamic control of a soft-body robot to deliver complex behaviors with low-dimensional actuation inputs is challenging. In this paper, we present a computational approach to automatically generate versatile, underactuated control policies that drives soft-bodied machines with complicated structures and nonlinear dynamics. Our target application is focused on the autonomous control of a soft multicopter, featured by its elastic material components, non-conventional shapes, and asymmetric rotor layouts, to precisely deliver compliant deformation and agile locomotion. The central piece of our approach lies in a lightweight neural surrogate model to identify and predict the temporal evolution of a set of geometric variables characterizing an elastic soft body. This physics-based learning model is further integrated into a Linear Quadratic Regulator (LQR) control loop enhanced by a novel online fixed-point relinearization scheme to accommodate the dynamic body balance, allowing an aggressive reduction of the computational overhead caused by the conventional full-scale sensing-simulation-control workflow. We demonstrate the efficacy of our approach by generating controllers for a broad spectrum of customized soft multicopter designs and testing them in a high-fidelity physics simulation environment. The control algorithm enables the multicopters to perform a variety of tasks, including hovering, trajectory tracking, cruising and active deforming.
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