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Trajectory Planning for Optimal Robot Catching in Real-Time

2011

Conference Paper

ei


Many real-world tasks require fast planning of highly dynamic movements for their execution in real-time. The success often hinges on quickly finding one of the few plans that can achieve the task at all. A further challenge is to quickly find a plan which optimizes a desired cost. In this paper, we will discuss this problem in the context of catching small flying targets efficiently. This can be formulated as a non-linear optimization problem where the desired trajectory is encoded by an adequate parametric representation. The optimizer generates an energy-optimal trajectory by efficiently using the robot kinematic redundancy while taking into account maximal joint motion, collision avoidance and local minima. To enable the resulting method to work in real-time, examples of the global planner are generalized using nearest neighbour approaches, Support Vector Machines and Gaussian process regression, which are compared in this context. Evaluations indicate that the presented method is highly efficient in complex tasks such as ball-catching.

Author(s): Lampariello, R. and Nguyen-Tuong, D. and Castellini, C. and Hirzinger, G. and Peters, J.
Journal: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011)
Pages: 3719-3726
Year: 2011
Month: May
Day: 0
Publisher: IEEE

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICRA.2011.5980114
Event Name: IEEE International Conference on Robotics and Automation (ICRA 2011)
Event Place: Shanghai, China

Address: Piscataway, NJ, USA
Digital: 0
ISBN: 978-1-61284-386-5
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{7048,
  title = {Trajectory Planning for Optimal Robot Catching in Real-Time},
  author = {Lampariello, R. and Nguyen-Tuong, D. and Castellini, C. and Hirzinger, G. and Peters, J.},
  journal = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011)},
  pages = {3719-3726 },
  publisher = {IEEE},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = may,
  year = {2011},
  doi = {10.1109/ICRA.2011.5980114  },
  month_numeric = {5}
}