Abstract
Obstacle avoidance for Dynamic Movement Primitives (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 system 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 obtaining 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 a dynamic environment.
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The presented framework is publicly available at https://github.com/mginesi/dmp_vol_obst.
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Funding
Open access funding provided by Università degli Studi di Verona within the CRUI-CARE Agreement. This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, ARS (Autonomous Robotic Surgery) project, grant agreement No. 742671.
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Conceptualization: Michele Ginesi. Data curation: Daniele Meli, Andrea Roberti. Formal Analysis: Michele Ginesi, Daniele Meli, Andrea Robeti. Funding acquisition: Paolo Fiorini. Investigation: Michele Ginesi, Daniele Meli, Andrea Roberti, Nicola Sansonetto. Methodology: Michele Ginesi, Daniele Meli, Andrea Roberti, Nicola Sansonetto. Project administration: Paolo Fiorini. Resources: Paolo Fiorini. Software: Michele Ginesi. Supervision: Nicola Sansonetto, Paolo Fiorini. Validation: Daniele Meli, Andrea Roberti. Visualization: Michele Ginesi, Daniele Meli, Andrea Roberti. Writing – original draft: Michele Ginesi, Daniele Meli. Writing – review and editing: Michele Ginesi, Daniele Meli, Nicola Sansonetto, Paolo Fiorini.
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Ginesi, M., Meli, D., Roberti, A. et al. Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions. J Intell Robot Syst 101, 79 (2021). https://doi.org/10.1007/s10846-021-01344-y
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DOI: https://doi.org/10.1007/s10846-021-01344-y