Merging Position and Orientation Motion Primitives
This addresses trajectory generation for robotics, but it appears incremental as it builds on existing motion primitive methods.
The paper tackled the problem of generating complex robotic trajectories by merging sequential motion primitives for positions and orientations, resulting in smooth pose trajectories with continuous transitions as demonstrated experimentally.
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.