CFD-based multi-objective controller optimization for soft robotic fish with muscle-like actuation
CFD-based multi-objective controller optimization for soft robotic fish with muscle-like actuation
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DOI：
10.1088/1748-3190/ab6dbb
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发表时间：
2020-05-01
影响因子：
3.4
通讯作者：
Gao, Tong
中科院分区：
文献类型：
--
作者：
Hess, Andrew;Tan, Xiaobo;Gao, Tong
软机器人利用丰富的非线性动力学和大自由度，通常通过超出传统刚性机器人能力的新颖手段来执行动作。然而，由于软机器人行为复杂，在分析、设计和优化方面存在相当大的挑战。对于软体机器人游泳者来说尤其如此，其动力学是由高度非线性的流体-结构相互作用决定的。我们提出了一个整体计算框架，采用多目标进化方法来优化软机器鱼在人工肌肉驱动下的机动反馈控制器。通过使用新颖的虚拟域/主动应变方法可以完全解决由此产生的流体-结构相互作用。特别地，我们考虑具有有限厚度的二维弹性板，其在身体两侧受到主动收缩应变。与需要指定全身曲率变化的传统方法相比，我们证明，局部施加收缩主动应变可以产生各种游泳步态，例如向前游泳和转身，使用更少的控制参数。一对比例积分微分（PID）控制器的参数分别用于控制主动应变的幅度和偏差，针对跟踪涉及不同轨迹和雷诺数的移动目标进行了优化，具有三个目标：跟踪误差、传输成本和弹性应变能。由此产生的多目标优化问题的帕累托前沿揭示了目标之间的相关性和权衡，并为软游泳器的设计和控制提供了重要的见解。
Soft robots take advantage of rich nonlinear dynamics and large degrees of freedom to perform actions often by novel means beyond the capability of conventional rigid robots. Nevertheless, there are considerable challenges in analysis, design, and optimization of soft robots due to their complex behaviors. This is especially true for soft robotic swimmers whose dynamics are determined by highly nonlinear fluid-structure interactions. We present a holistic computational framework that employs a multi-objective evolutionary method to optimize feedback controllers for maneuvers of a soft robotic fish under artificial muscle actuation. The resultant fluid-structure interactions are fully solved by using a novel fictitious domain/active strain method. In particular, we consider a two-dimensional elastic plate with finite thickness, subjected to active contractile strains on both sides of the body. Compared to the conventional approaches that require specifying the entire-body curvature variation, we demonstrate that imposing contractile active strains locally can produce various swimming gaits, such as forwarding swimming and turning, using far fewer control parameters. The parameters of a pair of proportional-integral-derivative (PID) controllers, used to control the amplitude and the bias of the active strains, respectively, are optimized for tracking a moving target involving different trajectories and Reynolds numbers, with three objectives, tracking error, cost of transport, and elastic strain energy. The resulting Pareto fronts of the multi-objective optimization problem reveal the correlation and trade-off among the objectives and offer key insight into the design and control of soft swimmers.