基于超光谱成像的机器人抓取系统
A Hyperspectral Imaging Guided Robotic Grasping System
AI总结：
本文提出一种基于超光谱成像的机器人抓取系统，通过PRISM和SpectralGrasp框架提升纺织品识别和分拣效率。
摘要：
超光谱成像是一种用于精确识别和分析材料或物体的先进技术。然而，其与机器人抓取系统整合的探索一直受到部署复杂性和高昂成本的限制。在本文中，我们介绍了一种新的基于超光谱成像的机器人抓取系统。该系统由PRISM（多面反射成像扫描机制）和SpectralGrasp框架组成。PRISM设计用于实现高精度、无失真的超光谱成像，同时简化系统集成和成本。SpectralGrasp通过有效利用超光谱图像中的空间和光谱信息生成机器人抓取策略。所提出系统在纺织品识别方面相比人类表现有显著提升，在与基于RGB的方法相比的分拣成功率上也有明显提高。此外，一系列比较实验进一步验证了该系统的有效性。研究强调了将超光谱成像与机器人抓取系统整合的潜在优势，展示了在复杂和动态环境中增强的识别和抓取能力。项目可在：https://zainzh.github.io/PRISM上获得。
英文摘要：
Hyperspectral imaging is an advanced technique for precisely identifying and analyzing materials or objects. However, its integration with robotic grasping systems has so far been explored due to the deployment complexities and prohibitive costs. Within this paper, we introduce a novel hyperspectral imaging-guided robotic grasping system. The system consists of PRISM (Polyhedral Reflective Imaging Scanning Mechanism) and the SpectralGrasp framework. PRISM is designed to enable high-precision, distortion-free hyperspectral imaging while simplifying system integration and costs. SpectralGrasp generates robotic grasping strategies by effectively leveraging both the spatial and spectral information from hyperspectral images. The proposed system demonstrates substantial improvements in both textile recognition compared to human performance and sorting success rate compared to RGB-based methods. Additionally, a series of comparative experiments further validates the effectiveness of our system. The study highlights the potential benefits of integrating hyperspectral imaging with robotic grasping systems, showcasing enhanced recognition and grasping capabilities in complex and dynamic environments. The project is available at: https://zainzh.github.io/PRISM.
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。
