BRIEF RESEARCH REPORT article
Front. Manuf. Technol.
Sec. Industrial Robotics and Automation
This article is part of the Research TopicAdvances in Industrial RoboticsView all 7 articles
Smart Placement, Faster Robots – A Comparison of Algorithms for Robot Base-Pose Optimization
Provisionally accepted
Matthias Mayer*
Matthias Althoff- Technical University of Munich, Munich, Germany
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Robotic automation is a key technology that increases the efficiency and flexibility of manufacturing processes. However, one of the challenges in deploying robots in novel environments is finding the optimal base pose for the robot, which affects its reachability and deployment cost. Yet, the existing research for automatically optimizing the base pose of robots has not been compared. We address this problem by optimizing the base pose of industrial robots with Bayesian optimization, exhaustive search, genetic algorithms, and stochastic gradient descent and find that all algorithms can reduce the cycle time for various evaluated tasks in synthetic and real-world environments. Stochastic gradient descent shows superior performance with regard to success rate solving over 90% of our real-world tasks, while genetic algorithms show the lowest final costs. All benchmarks and implemented methods are available as baselines against which novel approaches can be compared.
Keywords: performance evaluation and benchmarking, Methods and Tools for Robot System Design, Industrial robots, Base-PoseOptimization, Robot placement
Received: 06 Jun 2025; Accepted: 18 Nov 2025.
Copyright: © 2025 Mayer and Althoff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Matthias Mayer
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.