Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment
Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment
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DOI：
10.1109/lra.2022.3155821
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发表时间：
2022-02
影响因子：
5.2
通讯作者：
Shivani Kamtikar;Samhita Marri;Benjamin Walt;N. Uppalapati;Girish Krishnan;Girish V. Chowdhary
中科院分区：
文献类型：
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作者：
Shivani Kamtikar;Samhita Marri;Benjamin Walt;N. Uppalapati;Girish Krishnan;Girish V. Chowdhary
对于柔性连续体手臂，视觉伺服是一种流行的控制策略，其依赖于视觉反馈来闭合控制回路。然而，鲁棒视觉伺服具有挑战性，因为它需要从图像中提取可靠的特征，精确的控制模型和传感器来感知手臂的形状，这两者都很难在软机器人中实现。这封信通过提出一种基于深度神经网络的方法来规避这些挑战，该方法通过使用安装在手臂远端的摄像头进行视觉伺服来在软手臂上执行平滑和鲁棒的3D定位任务。卷积神经网络被训练来预测在结构化环境中实现所需姿势所需的驱动。综合和模块化的方法来估计从图像的致动，提出了实验比较。比例控制法的实施，以减少所需的和当前的图像之间的误差，看到的相机。该模型与比例反馈控制一起使所描述的方法对几种变化具有鲁棒性，例如新目标、照明、负载和软臂的减小。此外，该模型有助于以最小的努力将其转移到新的环境中。
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.