Fredrik Danielsson

About

Fredrik Danielsson is a leading researcher in flexible manufacturing automation, with a primary focus on Plug & Produce systems, multi-agent architectures, and robotic programming. His most significant contributions center on developing configurable multi-agent systems that enable manufacturing lines to dynamically add or remove resources without digital reconfiguration or reprogramming—a breakthrough that directly addresses the industry's need for adaptable, low-batch production. His work on goal-oriented process plans and skill-based resource definitions has been foundational, earning his top-cited paper 17 citations and his framework for Plug & Produce 14 citations. Danielsson has also advanced the field through virtual machine vision simulation for computer-aided robotics, multi-objective optimization of robotic press-line tending, and automated robot program generation for prefabrication in construction. His research integrates deep reinforcement learning with discrete event simulation for sustainable scheduling, demonstrating a commitment to both flexibility and energy efficiency. With over 100 total citations across his most influential works, Danielsson's contributions are shaping the future of reconfigurable, intelligent manufacturing systems that require minimal programming expertise.

Research Focus

Key Achievements

Top Papers

1. 1
   Customization and flexible manufacturing capacity using a graphical method applied on a configurable multi-agent system

   17 citations · 2022
2. 2
   Goal-Oriented Process Plans in a Multiagent System for Plug & Produce

   14 citations · 2020
3. 3
   Virtual Machine Vision in Computer Aided Robotics

   12 citations · 2009
4. 4
   Multi-objective constructive cooperative coevolutionary optimization of robotic press-line tending

   10 citations · 2016
5. 5
   From CAD to Plug & Produce

   10 citations · 2023
6. 6
   Verification and deployment of automatically generated robot programs used in prefabrication of house walls

   10 citations · 2018
7. 7
   Conceptual framework of scheduling applying discrete event simulation as an environment for deep reinforcement learning

   9 citations · 2022
8. 8
   Off-line programming of robots for metal deposition

   9 citations · 2005
9. 9
   Collaborative Robots to Support Flexible Operation in a Manufacturing System

   7 citations · 2012
10. 10
    Sheet-metal press line parameter tuning using a combined DIRECT and Nelder-Mead algorithm

    6 citations · 2011

Key Collaborators

Contact & Links