UR5 6-DoF robotic manipulator equipped with an RG2 gripper. Data and figure files for the paper: APO-MORL: An Adaptive Pareto-Optimal Framework for Re-al-Time Multi-Objective Optimization in Robotic Pick-and-Place Manufacturing Systems.
Year: 2025
Type: article
Abstract: Modern cyber-physical manufacturing systems face unprecedented challenges in dynamically balancing multiple conflicting operational objectives amid rapidly evolving production demands. Traditional optimization approaches, whether fixed- parameter control methods or static evolutionary algorithms, lack the adaptability required for real-time decision-making in Industry 4.0 environments where throughput, energy efficiency, precision, equipment longevity, and safety must be simultaneously optimized. This study presents a novel adaptive multi-objective reinforcement learning framework designed for intelligent manufacturing systems, with experimental validation through robotic pick-and-place operations as a representative industrial use case. The proposed approach integrates dynamic preference weighting mechanisms with Pareto-optimal policy discovery, enabling real-time adaptation to changing production priorities without manual reconfiguration. Validated in high-fidelity CoppeliaSim environments, the framework demonstrates significant performance improvements (+24.59% to +34.75% over baseline methods, p < 0.001) and achieves 95% optimal performance within 180 training episodes—representing a 5× faster convergence compared to evolutionary baselines. Critically, the framework demonstrates seamless integration capabilities with Manufacturing Execution Systems (MES), digital twins, and continual learning architectures, while maintaining edge computing compatibility (<2 GB RAM, <50 ms latency). This research advances cyber-physical systems by providing a scalable, real-time multi-objective optimization solution applicable across diverse manufacturing domains including assembly, quality control, flexible production, and human-robot collaboration, establishing new benchmarks for intelligent decision-making in next-generation sustainable manufacturing aligned with Industry 4.0 and 5.0 paradigms.
Source: Figshare
Author: Claudio Urrea, ORCID 0000-0001-7197-8928, University of Santiago of Chile
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084044982
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