用主动振动反馈提升机器人对物体软硬的识别能力
WaveTouch: Active Tactile Sensing Using Vibro-Feedback for Classification of Variable Stiffness and Infill Density Objects
- 通过主动注入振动并检测传播变化来判断物体软硬
- 软物吸收振动，硬物则增强信号，分类准确率高
- 适合需要精细抓取的柔性操作场景
机器人感知环境是执行抓取、滚动和力控等任务的基础。现有方法常因触觉系统易损或需移动物体而效率低。为此，本文设计一种基于主动振动反馈的触觉传感器，可在抓握过程中实时分类物体刚度。该方法通过人工注入振动，观察其在不同物理特性物体中的传播变化：软质物体吸收更多振动能量，而刚性物体不仅吸收少，还增强信号。实验表明，该方法可有效区分不同硬度与填充密度的物体，避免抓握损伤或滑落，相比被动振动感应更高效可靠。
原文摘要 · Abstract (English)
The perception and recognition of the surroundings is one of the essential tasks for a robot. With preliminary knowledge about a target object, it can perform various manipulation tasks such as rolling motion, palpation, and force control. Minimizing possible damage to the sensing system and testing objects during manipulation are significant concerns that persist in existing research solutions. To address this need, we designed a new type of tactile sensor based on the active vibro-feedback for object stiffness classification. With this approach, the classification can be performed during the gripping process, enabling the robot to quickly estimate the appropriate level of gripping force required to avoid damaging or dropping the object. This contrasts with passive vibration sensing, which requires to be triggered by object movement and is often inefficient for establishing a secure grip. The main idea is to observe the received changes in artificially injected vibrations that propagate through objects with different physical properties and molecular structures. The experiments with soft subjects demonstrated higher absorption of the received vibrations, while the opposite is true for the rigid subjects that not only demonstrated low absorption but also enhancement of the vibration signal.
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