An Environment-Adaptive Position/Force Control Based on Physical Property Estimation

TL;DR

This paper introduces an environment-adaptive control method that estimates physical properties to generate highly adaptable robot actions, improving reproducibility across diverse environments with minimal data and enhanced stability.

Contribution

It presents a novel impedance matching approach that adapts prerecorded actions to current environmental conditions, reducing data needs and ensuring stable, flexible robot control.

Findings

Outperforms existing motion reproduction systems in extreme impedance conditions

Requires only two sets of motion data, simplifying data collection

Ensures stability by leveraging existing control systems

Abstract

The current methods to generate robot actions for automation in significantly different environments have limitations. This paper proposes a new method that matches the impedance of two prerecorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for the position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position and force control, confirmed the superiority of the proposed method over existing motion reproduction system. The advantages of this method include the use of only two sets of motion data, significantly reducing the burden of data acquisition compared with machine-learning based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving the environmental adaptability of robots while simplifying the action generation method.