基于索驱动仿生机器眼最优控制的人眼视觉行为涌现
Emergence of human oculomotor behavior from optimal control of a cable-driven biomimetic robotic eye
摘要：
在人机交互中，眼动在非语言交流中起着重要的作用。然而，控制机器人眼睛的运动，显示类似的性能作为人类的视觉系统仍然是一个重大的挑战。在本文中，我们研究如何控制一个现实的模型，人眼与电缆驱动的驱动系统，模仿六个自由度的眼外肌。仿生设计引入了新的挑战，最值得注意的是需要控制每个肌肉上的预张力，以防止运动过程中张力的损失，这将导致电缆松弛和缺乏控制。我们建立了一个机器人原型，并开发了一个非线性模拟器和两个控制器。在第一种方法中，我们线性化的非线性模型，使用局部导数技术，并设计线性二次型最优控制器，以优化成本函数，占精度，能量消耗，和运动持续时间。第二种方法使用了一个递归神经网络，学习非线性系统动态从样本轨迹的系统，和一个非线性轨迹优化求解器，最小化类似的成本函数。我们专注于快速扫视眼球运动的生成与完全不受约束的运动学，以及同时满足几个动态优化标准的六根电缆的控制信号的生成。该模型忠实地模仿三维旋转运动学和动力学观察人类扫视。我们的实验结果表明，虽然这两种方法产生了类似的结果，非线性方法是更灵活的未来改进的模型，线性化模型的位置相关的预张力和局部导数的计算变得特别繁琐。
英文摘要：
In human-robot interactions, eye movements play an important role in non-verbal communication. However, controlling the motions of a robotic eye that display similar performance as the human oculomotor system is still a major challenge. In this paper, we study how to control a realistic model of the human eye with a cable-driven actuation system that mimics the six degrees of freedom of the extra-ocular muscles. The biomimetic design introduces novel challenges to address, most notably the need to control the pretension on each individual muscle to prevent the loss of tension during motion, that would lead to cable slack and lack of control. We built a robotic prototype and developed a nonlinear simulator and two controllers. In the first approach, we linearized the nonlinear model, using a local derivative technique, and designed linear-quadratic optimal controllers to optimize a cost function that accounts for accuracy, energy expenditure, and movement duration. The second method uses a recurrent neural network that learns the nonlinear system dynamics from sample trajectories of the system, and a non-linear trajectory optimization solver that minimizes a similar cost function. We focused on the generation of rapid saccadic eye movements with fully unconstrained kinematics, and the generation of control signals for the six cables that simultaneously satisfied several dynamic optimization criteria. The model faithfully mimics the three-dimensional rotational kinematics and dynamics observed for human saccades. Our experimental results indicate that while both methods yielded similar results, the nonlinear method is more flexible for future improvements to the model, for which the calculations of the linearized model's position-dependent pretensions and local derivatives become particularly tedious.
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