Klein Theresa J, Jeka John, Kiemel Tim, Lewis M Anthony
Department of Electrical and Computer Engineering, University of Arizona, Tucson, USA.
Biol Cybern. 2011 Dec;105(5-6):291-304. doi: 10.1007/s00422-011-0466-2. Epub 2012 Jan 31.
Most conventional robots rely on controlling the location of the center of pressure to maintain balance, relying mainly on foot pressure sensors for information. By contrast,humans rely on sensory data from multiple sources, including proprioceptive, visual, and vestibular sources. Several models have been developed to explain how humans reconcile information from disparate sources to form a stable sense of balance. These models may be useful for developing robots that are able to maintain dynamic balance more readily using multiple sensory sources. Since these information sources may conflict, reliance by the nervous system on any one channel can lead to ambiguity in the system state. In humans, experiments that create conflicts between different sensory channels by moving the visual field or the support surface indicate that sensory information is adaptively reweighted. Unreliable information is rapidly down-weighted,then gradually up-weighted when it becomes valid again.Human balance can also be studied by building robots that model features of human bodies and testing them under similar experimental conditions. We implement a sensory reweighting model based on an adaptive Kalman filter in abipedal robot, and subject it to sensory tests similar to those used on human subjects. Unlike other implementations of sensory reweighting in robots, our implementation includes vision, by using optic flow to calculate forward rotation using a camera (visual modality), as well as a three-axis gyro to represent the vestibular system (non-visual modality), and foot pressure sensors (proprioceptive modality). Our model estimates measurement noise in real time, which is then used to recompute the Kalman gain on each iteration, improving the ability of the robot to dynamically balance. We observe that we can duplicate many important features of postural sw ay in humans, including automatic sensory reweighting,effects, constant phase with respect to amplitude, and a temporal asymmetry in the reweighting gains.
大多数传统机器人依靠控制压力中心的位置来保持平衡，主要依靠足部压力传感器获取信息。相比之下，人类依靠来自多种来源的感官数据，包括本体感觉、视觉和前庭来源。已经开发了几种模型来解释人类如何协调来自不同来源的信息以形成稳定的平衡感。这些模型可能有助于开发能够利用多种感官来源更轻松地保持动态平衡的机器人。由于这些信息来源可能相互冲突，神经系统对任何一个通道的依赖都可能导致系统状态的模糊性。在人类中，通过移动视野或支撑表面在不同感官通道之间制造冲突的实验表明，感官信息会被自适应地重新加权。不可靠的信息会迅速被降低权重，然后在再次变得有效时逐渐被提高权重。也可以通过构建模拟人体特征的机器人并在类似的实验条件下对其进行测试来研究人类平衡。我们在一个双足机器人中实现了一个基于自适应卡尔曼滤波器的感官重新加权模型，并使其接受与用于人类受试者的类似的感官测试。与机器人中感官重新加权的其他实现方式不同，我们的实现方式包括视觉，即通过使用光流利用摄像头计算向前旋转（视觉模态），以及一个三轴陀螺仪来代表前庭系统（非视觉模态），还有足部压力传感器（本体感觉模态）。我们的模型实时估计测量噪声，然后在每次迭代时用于重新计算卡尔曼增益，提高机器人动态平衡的能力。我们观察到，我们可以复制人类姿势摆动的许多重要特征，包括自动感官重新加权、效应、相对于振幅的恒定相位以及重新加权增益中的时间不对称性。
