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Studenter på Högskolan Väst arbetar i ett automationslabb med en robotar.

Industrial automation has undergone rapid evolution, driven by advancements in technology and the pursuit of efficiency, safety, and productivity. From the early days of simple mechanical systems to today's sophisticated cyber-physical AI systems, the landscape of industrial automation continues to transform.  
Focus on University West: Smart, circular and human-centric industrial automation with focus on Plug & Produce. 

The research group have an edge within this field and emphasize:

  • Plug & Produce: Modular automation systems, where new products and resources can be seamlessly incorporated into an existing system without significant downtime or complex setup.
  • Smartness: AI systems, incorporating multi-agent technology, machine learning, and optimised decision-making.
  • Circularity: Automation systems that enable the reuse of resources (robots, machines, tools etc.) and products.
  • Human-Centric: Automation systems that are centred around (i) human interaction for production planning and (ii) collaborative features for smart and safe human-machine interaction.
  • Flexibility: Automation systems that quickly adapt to changing requirements, product variations, or external factors without the need for extensive reprogramming. 

The human-centric smart automation envisions the development of sustainable smart automation systems that integrate human perspectives. As a response to the lack of human-centric perspectives in I4.0, Industry 5.0 (I5.0) has recently arisen to balance and complement the dominating emphasis on technology by focusing on sustainability, human-centric, and resilience. At University West we work on self-adaptive systems that enhance the agility and interoperability of automated manufacturing to support equipment life extension.  The interoperability will be based on adaptable agents that collaborate by leveraging tailored Large Language Models (LLMs) to devise comprehensive solutions. 
Our research in AI-driven automation explores techniques such as deep learning, reinforcement learning, and generative adversarial networks to address real-world challenges in diverse industrial settings. We focus on AI based planning; AI planning involves the process of generating sequences of actions to achieve specific goals. The final goal is automation systems that require no programming but understanding of what the user wants. We utilize the performance of AI to reach the next level of automation. 


Mattias Bennulf

Mattias Bennulf Universitetslektor/biträdande PhD

Fredrik Danielsson

Fredrik Danielsson Professor Professor i automation

Mikael Ericsson

Mikael Ericsson Avdelningschef

Kristina Eriksson

Kristina Eriksson Senior lecturer Docent / Associate Professor in Production Systems

Mahmood Khabbazi

Mahmood Khabbazi Universitetslektor/biträdande

Bengt Lennartson

Bengt Lennartson Professor

Sudha Ramasamy

Sudha Ramasamy Senior lecturer

PhD students

Bassam Massouh

Bassam Massouh Doctoral student

Anders Nilsson

Anders Nilsson Research engineer MSC Robotik

Research engineers

Xiaoxiao Zhang

Xiaoxiao Zhang Research engineer