Static Shape Control of Soft Continuum Robots Using Deep Visual Inverse Kinematic Models
Static Shape Control of Soft Continuum Robots Using Deep Visual Inverse Kinematic Models
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DOI：
10.1109/tro.2023.3275375
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发表时间：
2023-08
影响因子：
7.8
通讯作者：
Elijah Almanzor;Fan Ye;Jialei Shi;T. G. Thuruthel;H. Wurdemann;F. Iida
中科院分区：
文献类型：
--
作者：
Elijah Almanzor;Fan Ye;Jialei Shi;T. G. Thuruthel;H. Wurdemann;F. Iida
柔性连续体机器人具有高度的灵活性和适应性，使其成为人体和农业等非结构化环境的理想选择。然而，它们的高度遵从性和可操作性使得它们很难建模、感知和控制。目前的控制策略主要集中在末端执行器的笛卡尔空间控制上，但很少有人对其进行全身控制。提出了一种新的基于图像的深度学习方法，用于软连续体机器人的闭环运动形状控制。该方法将图像空间中的局部逆运动学描述与深度卷积神经网络相结合，以实现对反馈噪声和连续臂中的机械变化具有鲁棒性的精确形状控制。Shape控制器实现起来既快速又简单；生成训练数据、训练网络和部署只需要几个小时，只需要一个网络摄像头进行反馈。这种方法提供了一种直观和用户友好的方法，通过遥操作仅使用期望目标状态的二维手绘图像来控制机器人的三维形状和构形，而不需要进一步的用户指令或考虑机器人的运动学。
Soft continuum robots are highly flexible and adaptable, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and maneuverability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space control of the end-effector, but few works have explored full-body control. This study presents a novel image-based deep learning approach for closed-loop kinematic shape control of soft continuum robots. The method combines a local inverse kinematics formulation in the image space with deep convolutional neural networks for accurate shape control that is robust to feedback noise and mechanical changes in the continuum arm. The shape controller is fast and straightforward to implement; it takes only a few hours to generate training data, train the network, and deploy, requiring only a web camera for feedback. This method offers an intuitive and user-friendly way to control the robot's 3-D shape and configuration through teleoperation using only 2-D hand-drawn images of the desired target state without the need for further user instruction or consideration of the robot's kinematics.