3 research outputs found
Merging Position and Orientation Motion Primitives
In this paper, we focus on generating complex robotic trajectories by merging
sequential motion primitives. A robotic trajectory is a time series of
positions and orientations ending at a desired target. Hence, we first discuss
the generation of converging pose trajectories via dynamical systems, providing
a rigorous stability analysis. Then, we present approaches to merge motion
primitives which represent both the position and the orientation part of the
motion. Developed approaches preserve the shape of each learned movement and
allow for continuous transitions among succeeding motion primitives. Presented
methodologies are theoretically described and experimentally evaluated, showing
that it is possible to generate a smooth pose trajectory out of multiple motion
primitives
Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
Stable dynamical systems are a flexible tool to plan robotic motions in
real-time. In the robotic literature, dynamical system motions are typically
planned without considering possible limitations in the robot's workspace. This
work presents a novel approach to learn workspace constraints from human
demonstrations and to generate motion trajectories for the robot that lie in
the constrained workspace. Training data are incrementally clustered into
different linear subspaces and used to fit a low dimensional representation of
each subspace. By considering the learned constraint subspaces as zeroing
barrier functions, we are able to design a control input that keeps the system
trajectory within the learned bounds. This control input is effectively
combined with the original system dynamics preserving eventual asymptotic
properties of the unconstrained system. Simulations and experiments on a real
robot show the effectiveness of the proposed approach