Human-Robot Collaboration using Variable Admittance Control and Human Intention Prediction
Human-Robot Collaboration using Variable Admittance Control and Human Intention Prediction
复制标题
使用可变导纳控制和人类意图预测的人机协作
DOI：
10.1109/case48305.2020.9217040
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jia Pan
中科院分区：
文献类型：
--
作者：
Wei;Zhe Hu;Jia Pan
由于人体肢体建模的困难，设计人机协作控制器具有很大的挑战性。本文提出了一种将变导纳控制与辅助控制相结合的新型控制器。特别地，强化学习用于通过最小化奖励函数来获得导纳控制器的最优阻尼值。此外，本文还利用长短期记忆网络（LSTM），根据人体肢体的动力学特性，预测人体的意图，并提出一种辅助控制器来帮助人体完成协作任务。我们验证了我们的预测算法和控制器的性能7 d.o.f弗兰卡Rankka机器人配备关节扭矩传感器。所设计的控制器既能实现最小加加速度轨迹，又能降低系统的运动成本。
Due to the difficulty of modeling human limb, it is very challenging to design the controller for human-robot collaboration. In this paper, we present a novel controller combining the variable admittance control and assistant control. In particular, the reinforcement learning is used to obtain the optimal damping value of the admittance controller by minimizing the reward function. In addition, we use the long short-term memory networks (LSTMs) to predict human intention based on the human limb dynamics and then an assistant controller is proposed to help human complete collaboration tasks. We validate the performance of our prediction algorithm and controller on a 7 d.o.f Franka Emika robot equipped with joint torque sensors. The proposed controller can both achieve minimum-jerk trajectory and low-effort cost.