The Advancements in tactile sensors and machine learning techniques open new opportunities for achieving intelligent grasping in robotics. Traditional robot is limited in its ability to perform autonomous grasping in unstructured environments. Although the existing robotic grasping method enhances the robot's understanding of its environment by incorporating visual perception, it still lacks the capability for force perception and force adaptation. Therefore, tactile sensors are integrated into robot hands to enhance the robot's adaptive grasping capabilities in various complex scenarios by tactile perception. This paper primarily discusses the adaption of different types of tactile sensors in robotic grasping operations and grasping algorithms based on them. By dividing robotic grasping operations into four stages: grasping generation, robot planning, grasping state discrimination, and grasping destabilization adjustment, a further review of tactile-based and tactile-visual fusion methods is applied in related stages. The characteristics of these methods are comprehensively compared with different dimensions and indicators. Additionally, the challenges encountered in robotic tactile perception is summarized and insights into potential directions for future research are offered. This review is aimed for offering researchers and engineers a comprehensive understanding of the application of tactile perception techniques in robotic grasping operations, as well as facilitating future work to further enhance the intelligence of robotic grasping.

中文翻译：

---

基于触觉感知的机器人智能抓取综述


触觉传感器和机器学习技术的进步为实现机器人智能抓取开辟了新的机遇。传统机器人在非结构化环境中自主抓取的能力受到限制。现有的机器人抓取方法虽然通过结合视觉感知增强了机器人对其环境的理解，但仍然缺乏力感知和力适应的能力。因此，将触觉传感器集成到机器人手中，通过触觉感知来增强机器人在各种复杂场景下的自适应抓取能力。本文主要讨论不同类型的触觉传感器在机器人抓取操作中的适应以及基于它们的抓取算法。通过将机器人抓取操作分为抓取生成、机器人规划、抓取状态判别和抓取不稳定调整四个阶段，进一步回顾了基于触觉和触觉-视觉融合方法在相关阶段的应用。从不同维度和指标上综合比较了这些方法的特点。此外，还总结了机器人触觉感知中遇到的挑战，并提供了对未来研究潜在方向的见解。本综述旨在让研究人员和工程师全面了解触觉感知技术在机器人抓取操作中的应用，并促进未来进一步增强机器人抓取智能化的工作。