Name
Abstract | ||
|---|---|---|
Teleoperated grasping requires the abilities to follow the intended trajectory from the user and autonomously search for a suitable pre-grasp pose relative to the object of interest. Challenges include dealing with uncertainty due to the noise of the teleoperator, human elements and calibration errors in the sensors. To address these challenges, an effective and robust algorithm is introduced to assist grasping during teleoperation. Although without premature object contact or regrasping strategies, the algorithm enables the robot to perform online adjustments to reach a pre-grasp pose before final grasping. We use three infrared (IR) sensors that are mounted on the robot hand, and design an algorithm that controls the robot hand to grasp objects using the information from the sensors' readings and the interface component. Finally, a series of experiments demonstrate that the system is robust when grasping a wide range of objects and tracking slow-moving mobile objects. Empirical data from a five-subject user study allows us to tune the relative contributions from the IR sensors and the interface component so as to achieve a balance of grasp assistance and teleoperation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1017/S0263574714000733 | ROBOTICA |
Keywords | Field | DocType |
Shared control,Grasping,Teleoperation,Human-robot interaction,IR sensor | Teleoperation,Computer vision,GRASP,Sensor array,Control engineering,Artificial intelligence,Engineering,Human–robot interaction | Journal |
Volume | Issue | ISSN |
33 | 4 | 0263-5747 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nutan Chen | 1 | 26 | 6.10 |
| Keng-Peng Tee | 2 | 1062 | 59.75 |
| Chee-Meng Chew | 3 | 375 | 40.58 |