SARAFun
SARAFun - Smart Assembly Robot with Advanced Functionalities
SARAFun targeted Breakthrough
The SARAFun project has been formed to enable a non-expert user to integrate a new bi-manual assembly task on a robot in less than a day. This will be accomplished by augmenting the robot with cutting edge sensory and cognitive abilities as well as reasoning abilities required to plan and execute an assembly task.
The research leading to these results has received funding from the European Commission’s Framework Programme Horizon 2020 – under grant agreement No 644938 – SARAFun.
Researchers: Rolf Johansson, Anders Robertsson, Fredrik Bagge Carlson, Martin Karlsson
Link to project: http://sarafun.eu
Over the last 30 years, robots have brought remarkable efficiency gains to industrial manufacturers, mainly in the automotive industry. Traditional industrial robots perform their assignments in cages and are heavily dependent on hard automation that requires pre-specified fixtures and time-consuming programming and reprogramming performed by experienced software engineers. The assembly application has always been considered as a promising robotic application but in reality it has proven challenging to automate due to e.g., complex materials, precise grasping requirements, part variations, operations requiring high precision (snap fits), operations requiring special motions (twist insertions) and wear and tear of the assembly equipment. While robotic assembly does exist, it has only been applied in a fraction of the potential cases. As a result, nowadays even expensive products produced in fairly large volumes, are still assembled manually in low wage countries under harsh conditions.
There is also a clear trend towards a shorter product lifetime. In order to be able to handle “burst” production (i.e., ramp up to full volume in very short time, run production for 3-12 months, and then change to new model) the lead time for setting up a production line/cell must be drastically reduced.
Publications
Martin Karlsson: Human–Robot Interaction Based on Motion and Force Control. PhD Thesis Department of Automatic Control, Lund University, Sweden, February 2019.
Fredrik Bagge Carlson: Machine Learning and System Identification for Estimation in Physical Systems. PhD Thesis Department of Automatic Control, Lund University, Sweden, December 2018.
Martin Karlsson, Anders Robertsson, Rolf Johansson: "Convergence of Dynamical Movement Primitives with Temporal Coupling". In 2018 European Control Conference, Limassol, Cyprus, June 2018.
Martin Karlsson, Anders Robertsson, Rolf Johansson: "Detection and Control of Contact Force Transients in Robotic Manipulation without a Force Sensor". In 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 2018.
Fredrik Bagge Carlson, Anders Robertsson, Rolf Johansson: "Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization". In The 14th IEEE International Conference on Control and Automation 2018, Anchorage, Alaska, United States, June 2018.
Fredrik Bagge Carlson, Mathias Haage: "YuMi low-level motion guidance using the Julia programming language and Externally Guided Motion Research Interface". Technical Report Department of Automatic Control, Lund University, Sweden, December 2017.
Fredrik Bagge Carlson: "BasisFunctionExpansions.jl : Basis Function Expansions for Julia". 2017.
Fredrik Bagge Carlson: "LPVSpectral.jl : A toolbox for least-squares spectral estimation and LPV spectral decomposition.". 2017.
Martin Karlsson: "On Motion Control and Machine Learning for Robotic Assembly". Licentiate Thesis Department of Automatic Control, Lund University, Sweden, July 2017.
Jacek Malec, Mathias Haage, Anders Nilsson, Maj Stenmark, Elin Anna Topp: "Semantic modelling of hybrid controllers for robotic cells". In International Conference on Flexible Automation and Intelligent Manufacturing, Modena, Italy, June 2017.
Martin Karlsson, Fredrik Bagge Carlson, Anders Robertsson, Rolf Johansson: "Two-Degree-of-Freedom Control for Trajectory Tracking and Perturbation Recovery during Execution of Dynamical Movement Primitives". IFAC-PapersOnLine, 50:1, pp. 1923–1930, 2017.
Martin Karlsson, Anders Robertsson, Rolf Johansson: "Autonomous Interpretation of Demonstrations for Modification of Dynamical Movement Primitives". In: IEEE International Conference on Robotics and Automation (ICRA), 2017, 2017.
Fredrik Bagge Carlson: "Modeling and Estimation Topics in Robotics". Licentiate Thesis Department of Automatic Control, Lund University, Sweden, March 2017.
Fredrik Bagge Carlson, Anders Robertsson, Rolf Johansson: "Linear Parameter-Varying Spectral Decomposition". In 2017 American Control Conference, Seattle, United States, May 2017.
Fredrik Bagge Carlson: "DifferentialDynamicProgramming.jl : A package for solving Differential Dynamic Programming and trajectory optimization problems.". 2016.
Fredrik Bagge Carlson: "DynamicMovementPrimitives.jl : Learning Dynamic Movement Primitives in Julia". 2016.
Maj Stenmark, Andreas Stolt, Elin A. Topp, Mathias Haage, Anders Robertsson, Klas Nilsson, Rolf Johansson: "The GiftWrapper: Programming a Dual-Arm Robot With Lead-through". In Human-Robot Interfaces for Enhanced Physical Interactions, Stockholm, Sweden, May 2016.
2020-05-26