Abstract
Robotic assistance presents an opportunity to benefit the lives of many adults with physical disabilities, yet accurately sensing the human body and tracking human motion remain difficult for robots. We present a multidimensional capacitive sensing technique capable of sensing the local pose of a human limb in real time. This sensing approach is unaffected by many visual occlusions that obscure sight of a person's body during robotic assistance, while also allowing a robot to sense the human body through some conductive materials, such as wet cloth. Given measurements from this capacitive sensor, we train a neural network model to estimate the relative vertical and lateral position to the closest point on a person's limb, as well as the pitch and yaw orientation between a robot's end effector and the central axis of the limb. We demonstrate that a PR2 robot can use this sensing approach to assist with two activities of daily living-dressing and bathing. Our robot pulled the sleeve of a hospital gown onto participants' right arms, while using capacitive sensing with feedback control to track human motion. When assisting with bathing, the robot used capacitive sensing with a soft wet washcloth to follow the contours of a participant's limbs and clean the surface of the body. Overall, we find that multidimensional capacitive sensing presents a promising approach for robots to sense and track the human body during assistive tasks that require physical human-robot interaction.
Abstract (translated)
机器人援助为许多身体残疾的成年人提供了一个机会，但精确地感知人体并跟踪人体运动对机器人来说仍然是困难的。我们提出了一种多维电容传感技术，能够实时感知人体肢体的局部姿势。这种传感方法不受许多视觉遮挡的影响，这些遮挡会在机器人协助过程中遮挡人体的视线，同时也允许机器人通过一些导电材料（如湿布）感知人体。根据电容传感器的测量结果，我们训练一个神经网络模型来估计到人肢体上最近点的相对垂直和横向位置，以及机器人末端执行器和肢体中心轴之间的俯仰和偏航方向。我们证明，一个PR2机器人可以使用这种传感方法，以协助两个日常生活穿衣和洗澡活动。我们的机器人将医院长袍的袖子拉到参与者的右臂上，同时使用电容式感应和反馈控制来跟踪人类的运动。当协助洗澡时，机器人用一块柔软的湿毛巾进行电容感应，跟踪参与者四肢的轮廓并清洁身体表面。总的来说，我们发现多维电容感测为机器人在需要物理机器人交互的辅助任务中感测和跟踪人体提供了一种很有前景的方法。
URL
https://arxiv.org/abs/1904.02111
