Soft-obstacle Avoidance for Redundant Manipulators with Recurrent Neural Network
Soft-obstacle Avoidance for Redundant Manipulators with Recurrent Neural Network
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DOI：
10.1109/iros.2018.8594346
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Yangming Li;B. Hannaford
中科院分区：
文献类型：
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作者：
Yangming Li;B. Hannaford
压缩软障碍物次要的受控运动任务是常见的人类。虽然这些任务对于遥控机器人来说几乎是微不足道的，但它们仍然是机器人自主性中具有挑战性的问题。解决这一问题意义重大。例如，在微创外科手术（MIS）中，安全地压缩软组织确保了手术安全性并减少了组织切除，从而显著减少了手术创伤和手术室时间，并导致改善的手术结果。在这项工作中，我们定义了软障碍物回避问题，并将安全运动约束投影到任务空间和速度空间中。我们说明了在机器人手术场景中解决这个问题的意义。我们提出了一个基于递归神经网络（RNNs）的解决方案，它将问题表示为不等式约束优化问题，并在其对偶空间中求解。所提出的方法的应用程序中演示的乌鸦II手术机器人。实验结果表明，该方法是有效的，在解决软障碍回避问题。
Compressing soft-obstacles secondary to a controlled motion task is common for human beings. While these tasks are nearly trivial for teleoperated robots, they remain a challenging problem in robotic autonomy. Addressing the problem is significant. For example, in Minimally Invasive Surgeries (MISs), safely compressing soft tissues ensures the surgical safety and decreases tissue removal, thus dramatically decreases surgical trauma and operating room time, and leads to improved surgical outcomes. In this work, we define the problem of soft-obstacle avoidance and project the safety motion constraints into the task space and the velocity space. We illustrate the significance of addressing this problem in the robotic surgery scenario. We present a Recurrent Neural Networks (RNNs) based solution, which formulates the problem as an inequality constrained optimization problem and solves it in its dual space. The application of the proposed method was demonstrated in the Raven II surgical robot. Experimental results demonstrated that the proposed method is effective in addressing the soft-obstacle avoidance problem.