For soft continuum arms, visual servoing is a popular control strategy that
relies on visual feedback to close the control loop. However, robust visual
servoing is challenging as it requires reliable feature extraction from the
image, accurate control models and sensors to perceive the shape of the arm,
both of which can be hard to implement in a soft robot. This letter circumvents
these challenges by presenting a deep neural network-based method to perform
smooth and robust 3D positioning tasks on a soft arm by visual servoing using a
camera mounted at the distal end of the arm. A convolutional neural network is
trained to predict the actuations required to achieve the desired pose in a
structured environment. Integrated and modular approaches for estimating the
actuations from the image are proposed and are experimentally compared. A
proportional control law is implemented to reduce the error between the desired
and current image as seen by the camera. The model together with the
proportional feedback control makes the described approach robust to several
variations such as new targets, lighting, loads, and diminution of the soft
arm. Furthermore, the model lends itself to be transferred to a new environment
with minimal effort.

本文提出了一种基于卷积神经网络的方法，针对软臂在结构化环境下实现视觉伺服平稳可靠的 3D 定位，通过神经网络对所需的位置控制器进行预测并使用比例控制法减少误差。