Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
This work addresses obstacle avoidance in robotics for applications like industrial manipulation and surgical tasks, representing an incremental improvement over the authors' prior method.
The authors tackled the problem of obstacle avoidance for Dynamic Movement Primitives by extending their previous superquadric potential framework to incorporate trajectory velocity, resulting in smoother behavior compared to state-of-the-art point-like methods and static potentials.
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.