PointGrasp：基于点云的抓取用于腱驱动软体机器人手套应用
PointGrasp: Point Cloud-based Grasping for Tendon-driven Soft Robotic Glove Applications
AI总结：
PointGrasp是一种基于点云的实时系统，通过分析物体几何形状来推断抓取意图，用于腱驱动软体手套辅助日常生活活动，在简单和复杂几何形状上分别达到0.8±0.39厘米和0.11±0.06厘米的RMSE。
摘要：
控制手部外骨骼以协助个体完成抓取任务，由于难以理解用户意图而构成挑战。我们提出，在日常生活活动（ADL）中的大多数日常抓取任务可以通过分析来自3D点云的物体几何形状（简单和复杂）来推断。本研究介绍了PointGrasp，一个用于语义识别家庭场景的实时系统，旨在在ADL期间为定制化的端到端抓取任务提供支持和增强辅助。该系统由一个RGB-D相机、一个惯性测量单元和一个集成在腱驱动软体机器人手套中的微处理器组成。RGB-D相机以超过每秒30帧的速率处理3D场景。所提出的流程在简单几何形状上平均均方根误差（RMSE）为0.8 ± 0.39厘米，在复杂几何形状上为0.11 ± 0.06厘米。在每种模式下，它都能识别并定位可触及的物体。该系统在端到端的视觉驱动的机器人辅助康复手部任务中显示出潜力。
英文摘要：
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 $\pm$ 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
