PointGrasp: Point Cloud-based Grasping for Tendon-driven Soft Robotic Glove Applications
This addresses the problem of assisting individuals with grasping tasks during activities of daily living, though it appears incremental as it builds on existing point cloud and robotic glove technologies.
The study tackled the challenge of controlling hand exoskeletons for grasping tasks by analyzing object geometries from 3D point clouds, achieving an average RMSE of 0.8 ± 0.39 cm for simple and 0.11 ± 0.06 cm for complex geometries.
Controlling hand exoskeletons to assist individuals with grasping tasks poses a challenge due to the difficulty in understanding user intentions. We propose that most daily grasping tasks during activities of daily living (ADL) can be deduced by analyzing object geometries (simple and complex) from 3D point clouds. The study introduces PointGrasp, a real-time system designed for identifying household scenes semantically, aiming to support and enhance assistance during ADL for tailored end-to-end grasping tasks. The system comprises an RGB-D camera with an inertial measurement unit and a microprocessor integrated into a tendon-driven soft robotic glove. The RGB-D camera processes 3D scenes at a rate exceeding 30 frames per second. The proposed pipeline demonstrates an average RMSE of 0.8 $\pm$ 0.39 cm for simple and 0.11 $\pm$ 0.06 cm for complex geometries. Within each mode, it identifies and pinpoints reachable objects. This system shows promise in end-to-end vision-driven robotic-assisted rehabilitation manual tasks.