基于深度学习的双臂机器人空中绳结打结
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning
AI总结：
针对柔性绳索状态动态变化导致动作难以预编程的问题，提出基于感觉运动数据训练双深度神经网络的双臂两指机器人空中打结方法，并通过真实单结与蝴蝶结实验验证其有效性。
摘要：
在本研究中，我们报告了基于深度学习的双臂两指机器人成功执行绳索空中打结。由于绳索具有柔性，在机器人操作过程中其状态不断变化。这要求机器人控制系统始终动态响应物体状态。然而，很难提前手动编写与所有物体状态相对应的合适机器人动作。为解决这一问题，我们构建了一个模型，利用两个深度神经网络指导机器人完成蝴蝶结和单结，这些网络通过从视觉和接近传感器等感觉运动中收集的数据进行训练。所得模型经验证能够基于在线获取的传感信息预测合适的机器人动作。此外，我们使用双臂两指机器人，基于 Ian 结方法设计了若干任务动作。所设计的打结动作不需要专用工作台或机器人手，从而提高了所提方法的通用性。最后，通过实验评估了真实机器人在绳索上执行单结和蝴蝶结时的打结性能及其成功率。实验结果证实了所提方法的有效性和高性能。
英文摘要：
In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the rope was in constant flux during the operation of the robot. This required the robot control system to dynamically correspond to the state of the object at all times. However, a manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. To resolve this issue, we constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor, including visual and proximity sensors. The resultant model was verified to be capable of predicting the appropriate robot motions based on the sensory information available online. In addition, we designed certain task motions based on the Ian knot method using the dual-arm two-fingers robot. The designed knotting motions do not require a dedicated workbench or robot hand, thereby enhancing the versatility of the proposed method. Finally, experiments were performed to estimate the knotting performance of the real robot while executing overhand knots and bowknots on rope and its success rate. The experimental results established the effectiveness and high performance of the proposed method.
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。
