Obstacle avoidance for DMPs is still a challenging problem. In our previous
work, we proposed a framework for obstacle avoidance based on superquadric
potential functions to represent volumes. In this work, we extend our previous
work to include the velocity of the trajectory in the definition of the
potential. Our formulations guarantee smoother behavior with respect to
state-of-the-art point-like methods. Moreover, our new formulation allows to
obtain a smoother behavior in proximity of the obstacle than when using a
static (i.e. velocity independent) potential. We validate our framework for
obstacle avoidance in a simulated multi-robot scenario and with different real
robots: a pick-and-place task for an industrial manipulator and a surgical
robot to show scalability; and navigation with a mobile robot in dynamic
environment.Comment: Preprint for Journal of Intelligent and Robotic System
