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Simulation-based procedure for robotic cell layout optimisation

delete2026-07-09
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PRE
AI
I
Iñigo Mendizabal-Arrieta *
J
Jairo R. Sánchez
S
Sara García
F
Fernando Torres
DOI:10.1080/09544828.2026.2698384delete
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Abstract

Abstract

En 中文
Robotic systems are a cornerstone of Industry 4.0, with adoption steadily increasing due to cost reductions and performance improvements. As these systems continuously interact with other elements in their surroundings, their performance strongly depends on parameters such as the pose, or shape of these components. Despite its importance, robotic system design often relies on manual methods or simplified simulations that yield suboptimal results. To address this gap, we present an optimiser-agnostic standardised simulation-based workflow for robotic system design optimisation. Within this workflow, we introduce two contributions: the use of high-fidelity simulations for motion-feasibility evaluation, ensuring that solutions remain valid with respect to detailed simulation-based collision constraints, and a fast hybrid two-step collision-checking approach where simulation-generated data is used to train a machine-learning-based self-collision classifier that reduces the required computational cost. The contributions are validated through two case studies: a mobile manipulator designed to unload a truck container filled with boxes, and a robotic cell integrating a horizontal lathe that must be loaded and unloaded with metallic cylinders, with input and output conveyors supplying and receiving the parts. Results demonstrate the existence of optimal poses for the elements in the scene. The computational benefit of the proposed collision checker is demonstrated.
Keywords:
Mathematical optimisation
high-fidelity simulations
collision checking
machine learning
robotic system design

Journal

J
Journal of Engineering Design
IF:
3.4
Papers:
161
Citations:
0

Organization

U
university of alicante
Scholars:
541
Papers: 221
Citations: 0
V
vicomtech foundation
Scholars:
10
Papers: 5
Citations: 0
Cited Papers

Cited Papers

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Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization
err2022-03-11
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errOAAI
errKazuki Nonoyama; Ziang Liu; Tomofumi Fujiwara; Md Moktadir Alam; Tatsushi Nishi
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