Abstract
In this paper, machine learning methods are proposed for human intention estimation based on the change of force distribution on the interaction surface during human-robot collaboration (HRC). The force distribution under different human intentions are examined when the human and robot are jointly carrying the same piece of object. A pair of Robotiq tactile sensors is applied to monitor the change of force distribution on the interaction surface. Three machine learning algorithms are tested on recognition of human intentions based on the force distribution patterns on the contact surface of grippers for the manipulator. The K-nearest Neighbor model is selected to build a real-time framework, which includes human intention estimation and cooperative motion planning for the robot manipulator. A real-time experiment is conducted to validate the method, which suggests the human intention estimation approach can help enhance the efficiency of HRC.
| Original language | English (US) |
|---|---|
| Title of host publication | Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications |
| Publisher | American Society of Mechanical Engineers |
| Volume | 2 |
| ISBN (Electronic) | 9780791858288 |
| DOIs | |
| State | Published - Jan 1 2017 |
| Event | ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States Duration: Oct 11 2017 → Oct 13 2017 |
Other
| Other | ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 |
|---|---|
| Country/Territory | United States |
| City | Tysons |
| Period | 10/11/17 → 10/13/17 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Mechanical Engineering