Balancing Cobot Productivity and Longevity Through Pre-Runtime Developer Feedback

In our experience, the task of optimizing robot longevity and efficiency is challenging due to the limited understanding and awareness developers' have about how their code influences a robot's expected lifespan. Unfortunately, acquiring the necessary information for computations is a complex task, and the data needed for these calculations remains unattainable until after runtime. In software engineering, traditional Static Code Analysis (SCA) techniques are applied to address such challenges. Although effective in identifying software anomalies and inefficiencies without execution, current SCA techniques do not adequately address the unique requirements of Cyber-Physical Systems (CPSs) in robotics. In this study, we propose a novel Machine Learning (ML) approach to assess robot program lines, considering the balance between speed and lifespan. Our solution, trained on data from 1325 operational collaborative robots (cobots) from the Universal Robots (UR) e-Series, classifies program lines concerning the expected lifespan of the robot, considering program line arguments, expected resource usage, and asserted joint stress. The model achieves a worst-case accuracy of 90.43% through 10-fold cross-validation with a 50% data split. We also present a selection of programming lines illustrating various robot program cases and an example of longevity improvement. Finally, we publish a dataset containing 56405 unique program line executions, aiming to enhance the sustainability and efficiency of robotic systems and support future research.