Postural Control in a Bipedal Robot using Sensory Reweighting
Postural control is a difficult problem for bipedal robots. Even in robots restricted to the sagittal plane, the system must react to falling forward or backward to stabilize itself during walking, standing, and during initiation and termination of walking. Most robots rely mainly on proprioceptive information such as foot pressure sensors and joint angle sensors for balance. By contrast, humans use a variety of sensory sources, including visual, vestibular, and proprioceptive sources to adapt fluidly to varying conditions. These sensory inputs combine to control posture but are “reweighted in response to changing conditions such as floor motion, visual scene motion, and degradation in vestibular sensitivity. Based on models of sensory reweighting in humans, we implement a sensory reweighting scheme in a bipedal robot using an adaptive Kalman filter. The adaptive filter uses an online estimate of the noise variance to adjust the Kalman gain depending on time-varying noise conditions. Thus, the robot automatically downweight sensory channels with unreliable data.
Theresa J. Klein M. Anthony Lewis John Jeka Tim Kiemel
Robotics and Neural Systems Lab,Department of Electrical and Computer Engineering,University of Ariz Department of Kinesiology,University of Maryland,College Park,MD
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
2053-2058
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）