Accurate and affordable cobot calibration without external measurement devices.

IF 7
Giovanni Franzese, Max Spahn, Jens Kober, Javier Alonso-Mora, Cosimo Della Santina
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Abstract

To reliably perform everyday tasks, collaborative robots must be accurate, not merely repeatable. Unfortunately, precise kinematic calibration often relies on tools that are more expensive than the robots themselves. We address this limitation by proposing a low-cost and effective calibration method aimed at democratizing cobot calibration. Our minimalist approach uses a single 3D-printable two-socket spherical-joint tool to kinematically constrain the robot end effector during data collection. An optimization routine updates the nominal kinematic model to ensure consistent socket predictions while preserving their mean distance. We validate the method on Franka, KUKA, and Kinova cobots, consistently reducing mean absolute errors, for example, from approximately 10 mm to 0.2 mm on Franka robots. To demonstrate practical impact, we further evaluate the calibrated model on a Franka robot in a peg-in-the-hole task with 0.4 mm tolerance and in a repeated drawing task using Cartesian control and learning from demonstration. Both tasks fail without calibration and consistently succeed with the calibrated model. The proposed method enables affordable and practical cobot calibration, providing a foundation for accurate manipulation tasks.

准确和负担得起的协作机器人校准,无需外部测量设备。
为了可靠地执行日常任务,协作机器人必须精确,而不仅仅是可重复。不幸的是,精确的运动学校准往往依赖于比机器人本身更昂贵的工具。我们通过提出一种低成本和有效的校准方法来解决这一限制,旨在使协作机器人校准大众化。我们的极简方法在数据收集过程中使用单个3d可打印的双插座球面关节工具来运动学约束机器人末端执行器。优化程序更新标称的运动学模型,以确保一致的套接字预测,同时保持他们的平均距离。我们在Franka、KUKA和Kinova协作机器人上验证了该方法,持续地减少了平均绝对误差,例如,在Franka机器人上从大约10毫米减少到0.2毫米。为了证明实际影响,我们进一步评估了校准模型在Franka机器人上的0.4 mm公差的钉孔任务和重复绘图任务,使用笛卡尔控制和从演示中学习。这两项任务在没有校准的情况下都失败了,并且在校准模型的情况下始终成功。该方法实现了经济实用的协作机器人校准,为精确的操作任务提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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