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arXiv 2012.06413cs.RO

一种面向球形软体机械臂的基于视觉的感知方法

A Vision-based Sensing Approach for a Spherical Soft Robotic Arm

ETH Zurich(苏黎世联邦理工学院)

Matthias Hofer, Carmelo Sferrazza, Raffaello D'Andrea

AI总结:

针对软体机械臂外部感知受限场景下的本体感受需求,提出一种集成相机的织物波纹管致动器视觉感知方案,通过卷积神经网络从图像预测姿态角,实现约1度精度的实时姿态估计,并经闭环控制验证了可靠性。

摘要:

感官反馈对于软体机器人系统的控制以及使其能够部署于各类不同任务至关重要。本体感受指感知机器人自身状态,对于将软体机器人系统部署在实验室环境之外(即没有动作捕捉系统等外部感知设备的场景)具有关键意义。\n 本文提出了一种面向织物材质软体机械臂的基于视觉的感知方法,该方法利用相机提供的高分辨率感官反馈,无需传感器与软体结构之间产生机械相互作用,因此得以保留软体系统的柔顺性。文中探讨了将相机集成到充气式织物波纹管致动器中的方案。三个各自集成了相机的致动器被用于控制球形机械臂,同时提供两个旋转自由度的感官反馈。一种卷积神经网络架构可根据相机图像预测描述机器人姿态的两个角度。在该监督学习方法的训练阶段及后续评估中,由动作捕捉系统提供基准真值数据。\n 这种基于相机的感知方法能够实时估计姿态,精度约为1度。研究通过利用感官反馈对机械臂姿态进行闭环控制,验证了该感知方法的可靠性。

英文摘要:

Sensory feedback is essential for the control of soft robotic systems and to enable deployment in a variety of different tasks. Proprioception refers to sensing the robot's own state and is of crucial importance in order to deploy soft robotic systems outside of laboratory environments, i.e. where no external sensing, such as motion capture systems, is available. A vision-based sensing approach for a soft robotic arm made from fabric is presented, leveraging the high-resolution sensory feedback provided by cameras. No mechanical interaction between the sensor and the soft structure is required and consequently, the compliance of the soft system is preserved. The integration of a camera into an inflatable, fabric-based bellow actuator is discussed. Three actuators, each featuring an integrated camera, are used to control the spherical robotic arm and simultaneously provide sensory feedback of the two rotational degrees of freedom. A convolutional neural network architecture predicts the two angles describing the robot's orientation from the camera images. Ground truth data is provided by a motion capture system during the training phase of the supervised learning approach and its evaluation thereafter. The camera-based sensing approach is able to provide estimates of the orientation in real-time with an accuracy of about one degree. The reliability of the sensing approach is demonstrated by using the sensory feedback to control the orientation of the robotic arm in closed-loop.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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