A Novel Trajectory Generation Method for Robot Control
Journal of Intelligent and Robotic Systems
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
Robotics and Autonomous Systems
Interaction learning for dynamic movement primitives used in cooperative robotic tasks
Robotics and Autonomous Systems
Movement primitives as a robotic tool to interpret trajectories through learning-by-doing
International Journal of Automation and Computing
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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. This paper presents a novel joining method that is based on the modification of the original dynamic movement primitive formulation. The new method can reproduce the target trajectory with high accuracy regarding both the position and the velocity profile and produces smooth and natural transitions in position space, as well as in velocity space. The properties of the method are demonstrated by its application to simulated handwriting generation, which are 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 a high potential for all robotics applications where trajectory joining is required.