论文标题
多功能肩sensing外套件中的非线性补偿用于实时遥控
Nonlinearity Compensation in a Multi-DoF Shoulder Sensing Exosuit for Real-Time Teleoperation
论文作者
论文摘要
柔软可穿戴机器人的合规性使其非常适合复杂的多个自由度（DOF）关节，但也引入了其他结构性非线性。对这些可穿戴机器人的直观控制需要强大的感应才能克服固有的非线性。本文提出了一个由生物启发的多功能肩外套的关节运动估计器，能够补偿遇到的非线性。为了克服诉讼的软性和合规性固有的非线性和滞后，我们开发了一种基于学习的深度方法，将传感器数据映射到关节空间。实验结果表明，新的基于学习的框架的表现优于最新的最新方法，同时仅使用基于GPU的边缘计算设备实现12ms推理时间。通过模拟的NAO人形机器人实时远程操作证明了我们组合的外套和学习框架的有效性。
The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi-DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.
