使用标准机器人夹爪的在线弹性估计与物料分拣
Online Elasticity Estimation and Material Sorting Using Standard Robot Grippers
AI总结：
本研究利用标准机器人夹爪和力传感器通过压缩估计材料弹性与粘弹性，明确了影响估计精度的因素，验证了单次抓取的材料区分能力，并在模拟回收场景中实现了多材质分拣。
摘要：
我们通过实验评估了利用两个标准平行爪夹爪、安装在机器人腕部的力/力矩传感器对物体进行压缩来估计材料属性的精度，实验以专业双轴压缩设备作为参考基准。我们获取了夹爪作用力与位移的关系曲线，并将其转换为应力-应变曲线。我们在不同应变点估计了弹性模量，并研究了多次压缩循环（预循环）、压缩速度以及夹爪表面积对估计结果的影响。研究通过压缩-解压循环中吸收的能量、Kelvin-Voigt模型以及Hunt-Crossley模型来估计粘弹性。我们发现：（1）更低的压缩速度能提升弹性估计的精度，而预循环和夹爪表面积对估计结果无显著影响；（2）即使经过校准，机器人夹爪在准确估计杨氏模量和粘弹性的绝对值方面能力有限；（3）不同夹爪得到的材料特性相对排序基本一致；（4）尽管可变形物体具有非线性特性，拟合线性应力-应变近似得到的结果比局部杨氏模量估计更稳定；（5）Hunt-Crossley模型在单次物体压缩的粘弹性估计中表现最佳。通过单次抓取得到的弹性和粘弹性估计值构成的二维空间，对区分物体材料属性具有优势。我们在模拟单流回收场景中验证了研究结果的适用性：即使在物体不同位置进行压缩，也能通过单次抓取正确分离塑料、纸张和金属物体。相关数据和代码已公开。
英文摘要：
We experimentally evaluated the accuracy with which material properties can be estimated through object compression by two standard parallel jaw grippers and a force/torque sensor mounted at the robot wrist, with a professional biaxial compression device used as reference. Gripper effort versus position curves were obtained and transformed into stress/strain curves. The modulus of elasticity was estimated at different strain points and the effect of multiple compression cycles (precycling), compression speed, and the gripper surface area on estimation was studied. Viscoelasticity was estimated using the energy absorbed in a compression/decompression cycle, the Kelvin-Voigt, and Hunt-Crossley models. We found that: (1) slower compression speeds improved elasticity estimation, while precycling or surface area did not; (2) the robot grippers, even after calibration, were found to have a limited capability of delivering accurate estimates of absolute values of Young's modulus and viscoelasticity; (3) relative ordering of material characteristics was largely consistent across different grippers; (4) despite the nonlinear characteristics of deformable objects, fitting linear stress/strain approximations led to more stable results than local estimates of Young's modulus; (5) the Hunt-Crossley model worked best to estimate viscoelasticity, from a single object compression. A two-dimensional space formed by elasticity and viscoelasticity estimates obtained from a single grasp is advantageous for the discrimination of the object material properties. We demonstrated the applicability of our findings in a mock single stream recycling scenario, where plastic, paper, and metal objects were correctly separated from a single grasp, even when compressed at different locations on the object. The data and code are publicly available.
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