PointGrasp: Point Cloud-based Grasping for Tendon-driven Soft Robotic Glove Applications
arxiv(2024)
摘要
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 ± 0.39 cm for simple
and 0.11 ± 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.
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