Evaluating Robot Program Performance with Power Consumption Driven Metrics in Lightweight Industrial Robots
Juan Heredia, Emil Stubbe Kolvig-Raun, Sune Lundo Sorensen, and Mikkel Baun Kjaergaard

TL;DR
This paper proposes a new framework for evaluating industrial robot performance by analyzing power consumption patterns, providing insights into energy efficiency and reliability beyond traditional CPU metrics, demonstrated through a case study with UR5e robots.
Contribution
Introduces a novel embodiment-based performance assessment framework using power metrics, diverging from conventional CPU-focused evaluations, to better reflect physical robot behavior.
Findings
Power-based metrics effectively categorize robot programs.
Energy efficiency varies across different programming strategies.
The approach supports sustainable manufacturing and cost reduction.
Abstract
The code performance of industrial robots is typically analyzed through CPU metrics, which overlook the physical impact of code on robot behavior. This study introduces a novel framework for assessing robot program performance from an embodiment perspective by analyzing the robot's electrical power profile. Our approach diverges from conventional CPU based evaluations and instead leverages a suite of normalized metrics, namely, the energy utilization coefficient, the energy conversion metric, and the reliability coefficient, to capture how efficiently and reliably energy is used during task execution. Complementing these metrics, the established robot wear metric provides further insight into long term reliability. Our approach is demonstrated through an experimental case study in machine tending, comparing four programs with diverse strategies using a UR5e robot. The proposed metrics…
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Taxonomy
TopicsManufacturing Process and Optimization · Flexible and Reconfigurable Manufacturing Systems · Mechatronics Education and Applications
