Kinesthetic teaching via Fast Marching Square
By Javier V. Gómez, David Álvarez, Santiago Garrido, Luis E. Moreno. 2012
12 citations recorded by the source corpus.
This paper presents a novel robotic learning technique based on Fast Marching Square (FM2). This method, which we have called FM Learning, is based on incorporating previous experience to the path planning system of the robot by taking into account paths taught to the robot via kinesthetic teaching, this is, guiding manually the robot through the desired path. The method proposed ensures that the path planning is always a globally asymptotically stable system at the target point, considering the motion as a nonlinear autonomous dynamical system. The few parameters the algorithm has can be tuned to get different behaviours of the learning system. The method has been evaluated through a set of simulations and also tested in the mobile manipulator Manfred V2.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Control and Systems Engineering
- Jochen J. Steil
- Klaus Neumann
- René Felix Reinhart
- Andre Lemme
- Harish Chaandar Ravichandar
- Ashwin P. Dani
- Iman Salehi
Citations in the corpus
- Neural learning of vector fields for encoding stable dynamical systems (2014, background): “Wave propagation [10], which was introduced to dynamic path planning in [11], can be used to create potential fields, which in turn can be adapted to demonstrations [12].”