A Universal Tool Interaction Force Estimation Approach for Robotic Tool Manipulation

TL;DR

This paper introduces a new method to estimate forces at the end of robotic tools in real time, improving precision and adaptability for different tools and grippers.

Contribution

A universal tool-end interaction force estimation approach using a dynamic model and spiking neural networks for real-time compensation of sensor uncertainties.

Findings

The method achieved root mean square errors below 0.5 N for force axes and 0.03 Nm for torque axes.

The approach was successfully applied to robotic scraper manipulation with precise and rapid interaction force feedback.

Abstract

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with x,
y, and z axes and 0.03 Nm with τx, τy, and τz axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly.

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