Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
| Autor: | Nicola Sansonetto, Paolo Fiorini, Michele Ginesi, Daniele Meli, Andrea Roberti |
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| Jazyk: | angličtina |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer science 02 engineering and technology Industrial and Manufacturing Engineering Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence Control theory Obstacle avoidance 0202 electrical engineering electronic engineering information engineering Dynamic Movement Primitives Electrical and Electronic Engineering Obstacle Avoidance Movement (music) Mechanical Engineering Work (physics) Mobile robot Learning From Demonstration Task (computing) Control and Systems Engineering Obstacle Scalability Robot 020201 artificial intelligence & image processing Robotics (cs.RO) Software |
| Zdroj: | Journal of Intelligent & Robotic Systems. 101(4) |
| ISSN: | 1573-0409 0921-0296 |
| DOI: | 10.1007/s10846-021-01344-y |
| Popis: | 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. Preprint for Journal of Intelligent and Robotic Systems |
| Databáze: | OpenAIRE |
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