Dynamic movement primitives: volumetric obstacle avoidance
| Autor: | Nicola Sansonetto, Diego Dall'Alba, Andrea Calanca, Daniele Meli, Michele Ginesi, Paolo Fiorini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
superquadric potential Computer science Movement (music) business.industry Open problem Work (physics) learning from demonstration 02 engineering and technology Time duration Ellipsoid Dynamic movement primitives obstacle avoidance superquadric potential learning from demonstration obstacle avoidance 020901 industrial engineering & automation Obstacle avoidance Dynamic movement primitives 0202 electrical engineering electronic engineering information engineering Trajectory Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | 2019 19th International Conference on Advanced Robotics (ICAR) ICAR |
| Popis: | Dynamic Movement Primitives (DMPs) are a framework for learning a trajectory from a demonstration. The trajectory can be learned efficiently after only one demonstration, and it is immediate to adapt it to new goal positions and time duration. Moreover, the trajectory is also robust against perturbations. However, obstacle avoidance for DMPs is still an open problem. In this work, we propose an extension of DMPs to support volumetric obstacle avoidance based on the use of superquadric potentials. We show the advantages of this approach when obstacles have known shape, and we extend it to unknown objects using minimal enclosing ellipsoids. A simulation and experiments with a real robot validate the framework, and we make freely available our implementation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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