用视觉触觉传感实现水果硬度快速无损测量，提升机器人抓取精度。
Quantitative Hardness Assessment with Vision-based Tactile Sensing for Fruit Classification and Grasping
- 通过视觉触觉传感器捕捉接触时的法向力变化，推算硬度。
- 单次接触即可完成评估，对多种水果和成熟度均有效。
- 适合农业机器人实时自适应抓取，减少果实损伤。
精确估计水果硬度对于自动化分类与处理系统至关重要，尤其在判断品种、评估成熟度及确定采摘力度方面。本研究提出一种基于视觉触觉传感的定量硬度评估新框架，专为农业机器人应用设计。该方法通过视觉触觉传感器获取法向力，并根据其动态特性计算硬度，实现快速、非破坏性的一次接触评估。该框架集成至机器人系统后，可提升抓取力的实时自适应能力，显著降低果实损伤风险。此外，基于平均法向力动态的通用判据使其在多种水果类型与尺寸下均具有效性。在不同水果种类及成熟度追踪实验中广泛验证，结果表明该框架具有优异的效能与鲁棒性，是自动化水果处理领域的重要进展。
原文摘要 · Abstract (English)
Accurate estimation of fruit hardness is essential for automated classification and handling systems, particularly in determining fruit variety, assessing ripeness, and ensuring proper harvesting force. This study presents an innovative framework for quantitative hardness assessment utilizing vision-based tactile sensing, tailored explicitly for robotic applications in agriculture. The proposed methodology derives normal force estimation from a vision-based tactile sensor, and, based on the dynamics of this normal force, calculates the hardness. This approach offers a rapid, non-destructive evaluation through single-contact interaction. The integration of this framework into robotic systems enhances real-time adaptability of grasping forces, thereby reducing the likelihood of fruit damage. Moreover, the general applicability of this approach, through a universal criterion based on average normal force dynamics, ensures its effectiveness across a wide variety of fruit types and sizes. Extensive experimental validation conducted across different fruit types and ripeness-tracking studies demonstrates the efficacy and robustness of the framework, marking a significant advancement in the domain of automated fruit handling.
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