Summary

The generation of complex movement patterns, in particular in cases where one needs to smoothly and accurately join trajectories in a dynamic way, is an important problem in robotics. In this work we present a novel joining method based on the modification of the original dynamic movement primitive (DMP) formulation. The new method can reproduce the target trajectory with high accuracy regarding both, position and velocity profile, and produces smooth and natural transitions in position as well as velocity space. The properties of the method are demonstrated by applying it to simulated handwriting generation also shown on a robot, where an adaptive algorithm is used to learn trajectories from human demonstration. These results demonstrate that the new method is a feasible alternative for joining of movement sequences which has high potential for all robotics applications where trajectory joining is required.