MULTI-AGENT COOPERATIVE CONTROL ARCHITECTURE FOR AUTONOMOUS INDUSTRIAL ROBOTS IN SMART MANUFACTURING ENVIRONMENTS
Fuente:
Zenodo
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866901165661224960 |
|---|---|
| author | Min-Jae Kwon Seo-Yeon Lim Dong-Hyun Bae |
| author_facet | Min-Jae Kwon Seo-Yeon Lim Dong-Hyun Bae |
| contents | <p><strong><span lang="EN-US">Abstract</span></strong></p> <p><span lang="EN-US">The rapid evolution of smart manufacturing systems has fundamentally transformed the operational paradigm of industrial robotics. Unlike traditional automation frameworks characterized by rigid, centralized programming, modern production environments require adaptive, cooperative robotic agents capable of decentralized decision-making and dynamic task redistribution. As production lines become increasingly modular and demand variability intensifies, robotic systems must exhibit autonomy not only at the motion control level but also at the coordination and strategic planning levels.</span></p> <p><span lang="EN-US">This paper proposes a Multi-Agent Cooperative Control Architecture (MACCA) designed to enable distributed intelligence among heterogeneous industrial robots operating within Industry 4.0 environments. The proposed framework integrates graph-theoretic consensus control, deep reinforcement learning, distributed trajectory planning, and edge-based computational autonomy. Each robotic unit functions as an intelligent agent equipped with local sensing, onboard processing capabilities, and peer-to-peer communication modules.</span></p> <p><span lang="EN-US">The architecture introduces a hierarchical yet decentralized control mechanism in which low-level motor control remains embedded within robotic platforms, mid-level coordination emerges through distributed negotiation protocols, and high-level optimization is performed through supervisory analytics without imposing centralized control authority. The system is evaluated through a simulated smart factory environment incorporating autonomous mobile robots, articulated manipulators, and collaborative robotic arms. Experimental results demonstrate significant improvements in operational efficiency, collision mitigation, and energy optimization under dynamic production loads.</span></p> <p><span lang="EN-US">The proposed architecture contributes to applied robotics engineering by offering a scalable and fault-tolerant cooperative framework capable of sustaining industrial-grade real-time performance while reducing dependence on centralized infrastructure.</span></p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18863513 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | MULTI-AGENT COOPERATIVE CONTROL ARCHITECTURE FOR AUTONOMOUS INDUSTRIAL ROBOTS IN SMART MANUFACTURING ENVIRONMENTS Min-Jae Kwon Seo-Yeon Lim Dong-Hyun Bae Multi-agent systems; distributed robotics; cooperative control; smart manufacturing; consensus algorithms; reinforcement learning; industrial automation; decentralized coordination; autonomous agents <p><strong><span lang="EN-US">Abstract</span></strong></p> <p><span lang="EN-US">The rapid evolution of smart manufacturing systems has fundamentally transformed the operational paradigm of industrial robotics. Unlike traditional automation frameworks characterized by rigid, centralized programming, modern production environments require adaptive, cooperative robotic agents capable of decentralized decision-making and dynamic task redistribution. As production lines become increasingly modular and demand variability intensifies, robotic systems must exhibit autonomy not only at the motion control level but also at the coordination and strategic planning levels.</span></p> <p><span lang="EN-US">This paper proposes a Multi-Agent Cooperative Control Architecture (MACCA) designed to enable distributed intelligence among heterogeneous industrial robots operating within Industry 4.0 environments. The proposed framework integrates graph-theoretic consensus control, deep reinforcement learning, distributed trajectory planning, and edge-based computational autonomy. Each robotic unit functions as an intelligent agent equipped with local sensing, onboard processing capabilities, and peer-to-peer communication modules.</span></p> <p><span lang="EN-US">The architecture introduces a hierarchical yet decentralized control mechanism in which low-level motor control remains embedded within robotic platforms, mid-level coordination emerges through distributed negotiation protocols, and high-level optimization is performed through supervisory analytics without imposing centralized control authority. The system is evaluated through a simulated smart factory environment incorporating autonomous mobile robots, articulated manipulators, and collaborative robotic arms. Experimental results demonstrate significant improvements in operational efficiency, collision mitigation, and energy optimization under dynamic production loads.</span></p> <p><span lang="EN-US">The proposed architecture contributes to applied robotics engineering by offering a scalable and fault-tolerant cooperative framework capable of sustaining industrial-grade real-time performance while reducing dependence on centralized infrastructure.</span></p> <p> </p> |
| title | MULTI-AGENT COOPERATIVE CONTROL ARCHITECTURE FOR AUTONOMOUS INDUSTRIAL ROBOTS IN SMART MANUFACTURING ENVIRONMENTS |
| topic | Multi-agent systems; distributed robotics; cooperative control; smart manufacturing; consensus algorithms; reinforcement learning; industrial automation; decentralized coordination; autonomous agents |
| url | https://doi.org/10.5281/zenodo.18863513 |