Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

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.

arXiv commentPreprint for Journal of Intelligent and Robotic Systems

References in corpus (4)

• Overcoming Some Drawbacks of Dynamic Movement Primitives
• Learning Feedback Terms for Reactive Planning and Control
• Improving rigid 3D calibration for robotic surgery
• Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

Cited by in corpus (6)

• Dynamic Movement Primitives in Robotics: A Tutorial Survey
• Overcoming Some Drawbacks of Dynamic Movement Primitives
• Learning Stable Robotic Skills on Riemannian Manifolds
• Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview
• Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives
• Unsupervised identification of surgical robotic actions from small non homogeneous datasets