Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

TL;DR

This paper extends dynamic movement primitives with velocity-dependent potential functions for volumetric obstacle avoidance, resulting in smoother trajectories in complex environments, validated through simulations and real robot experiments.

Contribution

It introduces a velocity-inclusive potential function for DMPs, improving obstacle avoidance smoothness and scalability in dynamic multi-robot scenarios.

Findings

Smoother obstacle avoidance trajectories achieved.

Effective in multi-robot and real robot tasks.

Handles dynamic environments successfully.

Abstract

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.