EURO 2025 Leeds
Abstract Submission

2429. Energy-Efficient Motion Planning for Industrial Robots using Digital Twins

Invited abstract in session TB-20: Applications of combinatorial optimisation in industry and services 2, stream Combinatorial Optimization.

Tuesday, 10:30-12:00
Room: Esther Simpson 2.11

Authors (first author is the speaker)

1. Dimitrios Tsakoumis
Industrial Management and Technology, University of Piraeus
2. Gregory Koronakos
Department of Informatics, University of Piraeus
3. Stathis Plitsos
Industrial Management and Technology, University of Piraeus
4. Johannes Feik
FFT Produktionssysteme GmbH & Co. KG
5. Pavlos Eirinakis
Industrial Management and Technology, University of Piraeus

Abstract

The growing demand for energy-efficient robotic systems in manufacturing necessitates advanced optimization techniques that balance performance and computational feasibility. A promising approach involves leveraging Digital Twin (DT)-based methodologies to model and optimize robotic arm movements. This work presents an Integer Programming model that incorporates a set of operational scenarios, each characterized by varying movement attributes such as velocity, acceleration, and trajectory type, all of which influence energy consumption and task completion time. Given the large solution space, a preprocessing method based on Pareto dominance is introduced to filter out strictly suboptimal scenarios. By retaining only Pareto-optimal scenarios, which represent the best trade-offs between energy efficiency and time, the solution space is reduced without compromising the quality of the solution with respect to optimal energy consumption. Computational experiments demonstrate the scalability and efficiency of the proposed approach. Results show that the use of Pareto filtering significantly reduces computational time, making it feasible to solve large-scale optimization problems that would otherwise be intractable. The integration of DT-based scenario generation with optimization tools presents a significant advancement in improving robotic energy efficiency and decision-making in automated production systems.

Keywords

Status: accepted

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