Dynamic force guidance improves robot teaching and work efficiency
A Unified Dynamic Force Guidance Framework for Performance-Optimized Kinesthetic Teaching
Robotics
Summary
Collaborative robots need to be programmed quickly when products change often, but users might not set the robot's position well during teaching, reducing performance. The authors created a system that uses gentle force feedback to help users move the robot into better positions while keeping its ability to perform tasks strong. Tests with a six-joint robot showed that this approach makes the robot work more efficiently when repeating tasks later. This can save time in industrial production.
What this means in practice
- •For industrial robot programmers: Improve robot teaching sessions by guiding users to optimal robot configurations, increasing operation quality and efficiency during automated tasks.
- •For manufacturing line engineers: Design workflow setups that maintain robot performance above thresholds to reduce production cycle times and increase throughput during frequent product changeovers.
Authors
Chunxin Li, Jianhua Wu, Zhenhua Xiong, Xiangyang Zhu
Abstract
Collaborative robots are increasingly deployed in industrial scenarios characterized by frequent product changeovers. As an intuitive programming method, kinesthetic teaching facilitates rapid robot deployment. However, users may overlook the configuration of the robot during kinesthetic teaching, leading to degradation in operational performance. Operational performance refers to the capability of the robot to generate motion and can be quantified by the Minimum Singular Value of the Jacobian matrix. To address this issue, this paper proposes an online dynamic force guidance method that integrates performance constraint and optimization mechanisms. Specifically, variable admittance control maintains the operational performance of the robot above a predefined threshold, while a virtual force actively guides the user to drag the robot towards configurations with improved performance. Experiments are conducted on a 6-DOF collaborative robot, comparing three typical paths in the task space. To evaluate the quality of the taught trajectories, trajectory playback experiments are conducted to analyze the relationship between the operational performance of the robot and the work efficiency. The results demonstrate that the proposed method effectively enhances the operational performance of the robot and consequently improves the work efficiency, holding significant value for reducing production takt time in industrial deployment.