Human–Robot Collaboration Based on Motion Intention Estimation

By Yanan Li, Shuzhi Sam Ge. IEEE/ASME transactions on mechatronics, 2014

296 citations recorded by the source corpus.

In this paper, adaptive impedance control is proposed for a robot collaborating with a human partner, in the presence of unknown motion intention of the human partner and unknown robot dynamics. Human motion intention is defined as the desired trajectory in the limb model of the human partner, which is extremely difficult to obtain considering the nonlinear and time-varying property of the limb model. Neural networks are employed to cope with this problem, based on which an online estimation method is developed. The estimated motion intention is integrated into the developed adaptive impedance control, which makes the robot follow a given target impedance model. Under the proposed method, the robot is able to actively collaborate with its human partner, which is verified through experiment studies.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/efaa81ca8ab5e3c160367df076f1230686962326

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Marcia Kilchenman O'Malley
2. Wei He
3. Edoardo Battaglia
4. Craig G. Mcdonald
5. Haifeng Huang
6. Dylan P. Losey
7. Shuzhi Sam Ge

In Cognitive Neuroscience

1. Marcia Kilchenman O'Malley
2. Edoardo Battaglia
3. Craig G. Mcdonald
4. Dylan P. Losey

In Control and Systems Engineering

1. Wei He
2. Haifeng Huang
3. Shuzhi Sam Ge
4. Marcia Kilchenman O'Malley
5. Dylan P. Losey
6. Shaocheng Tong
7. Mou Chen
8. Yanjun Liu

Citations in the corpus

• A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction (2018, background): “Ge and coworkers [25,26] define the motion intention in this way for a human and robot performing collaborative motions with shared end-effector position, as do Erden and Tomiyama [27].”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, background): “In [21], adaptive impedance control is proposed for a robot collaborating with a human partner, in the presence of unknown robot dynamics.”