Geometrically optimal gaits: a data-driven approach
Geometrically optimal gaits: a data-driven approach
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DOI：
10.1007/s11071-018-4466-9
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发表时间：
2018-11-01
影响因子：
5.6
通讯作者：
Revzen, Shai
中科院分区：
文献类型：
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作者：
Bittner, Brian;Hatton, Ross L.;Revzen, Shai
对动物或机器人最佳运动的研究通常涉及在使用大量参数指定的循环形状变化或步态空间中寻求最佳性。我们展示了一种数据驱动的方法，通过利用动力学的几何特性有效地构建系统的局部模型，然后使用该模型快速计算梯度，计算成本函数相对于大量步态参数的梯度。我们的建模步骤特别适用于由几何力学中的类连接模型控制的系统，其中包含许多高摩擦状态。我们通过在低雷诺数（粘性摩擦）环境中模拟平面多节蛇状游泳者，展示了如何使用我们的方法在嘈杂、类似实验的条件下优化步态。我们的优化结果恢复了具有 66 维步态参数化的 3 节段游泳者的已知结果，并扩展到优化具有 264 维步态空间的 9 节段游泳者的运动，仅使用 30 个模拟试验，每个模拟试验有 30 个步态周期。我们提出的数据驱动的几何步态优化方法旨在对噪声、随机扰动动力学（如实验数据那样的噪声和变量）进行操作，并有效地优化大量参数。我们相信这种方法有可能显着提高我们利用硬件在环优化机器人步态的能力，并研究动物步态相对于假设成本函数的最优性。
The study of optimal motion of animals or robots often involves seeking optimality over a space of cyclic shape changes, or gaits, specified using a large number of parameters. We show a data-driven method for computing the gradient of a cost functional with respect to a large number of gait parameters by employing geometric properties of the dynamics to efficiently construct a local model of the system, and then using this model to rapidly compute the gradients. Our modeling step specifically applies to systems governed by connection-like models from geometric mechanics, which encompass a number of high-friction regimes. We demonstrate using our method for optimizing gaits under noisy, experiment-like conditions by simulating planar multi-segment serpent-like swimmers in a low Reynolds number (viscous friction) environment. Our optimization results recover known results for 3-segment swimmers with a 66 dimensional gait parameterization, and extend to optimizing the motion of a 9 segment swimmer with a 264 dimensional gait space, using only 30 simulation trials of 30 gait cycles each. The data-driven geometric gait optimization approach we present is designed to operate on noisy, stochastically perturbed dynamicsas noisy and variable as experimental dataand efficiently optimize a large number of parameters. We believe this approach has the potential to significantly advance our ability to optimize robot gaits with hardware in the loop and to study the optimality of animal gaits with respect to hypothesized cost functions.