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Heiko Renz edited this page Dec 19, 2023 · 1 revision

This page provides information about used references inside this project or by the dependencies. Note that not all references to the dependencies are listed here. For further details, please refer to the corresponding project.

Furthermore, we add some information about the usage of the listed references.

  • M. Krämer, C. Rösmann, F. Hoffmann, and T. Bertram, “Model predictive control of a collaborative manipulator considering dynamic obstacles,” Optimal Control Applications and Methods, vol. 41, no. 4, pp. 1211–1232, 2020, doi: 10.1002/oca.2599.

    → This reference provides additional information about the general approach of the planner. Furthermore, it includes information about the used potential functions and the used optimization problem.

  • C. Rösmann, M. Krämer, A. Makarow, F. Hoffmann, and T. Bertram, “Exploiting sparse structures in nonlinear model predictive control with hypergraphs,” in AIM 2018: 9-12 July 2018, Auckland, New Zealand : IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Piscataway, 2018, pp. 1332–1337. doi: 10.1109/AIM.2018.8452378.

    → This reference provides additional information about the hypergraph strategy used for faster solutions to the optimization problem.

  • S. Calinon, F. Guenter, and A. Billard, “On Learning, Representing, and Generalizing a Task in a Humanoid Robot,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, pp. 286–298, Apr. 2007, doi: 10.1109/TSMCB.2006.886952.

    and

  • S. Calinon, Z. Li, T. Alizadeh, N. G. Tsagarakis, and D. G. Caldwell, “Statistical dynamical systems for skills acquisition in humanoids,” in 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012): Osaka, Japan, 29 November - 1 December 2012: IEEE, 2012, pp. 323–329. doi: 10.1109/HUMANOIDS.2012.6651539.

    → These references provide additional information about the GMM approach used for uncertainty estimation of human motion extrapolations. Furthermore, some parts of the provided code from S. Calinon are adapted in this project (https://calinon.ch/codes_prev.htm).

  • E. Aksan, M. Kaufmann, and O. Hilliges, “Structured Prediction Helps 3D Human Motion Modelling,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South): IEEE, Oct. 2019, pp. 7143–7152. doi: 10.1109/ICCV.2019.00724.

    → This reference provides additional information about the used neural network approach for human motion prediction. Furthermore, some parts of the provided code from E. Aksan are used in this project (see Dependencies).

  • P. R. Amestoy, I. S. Duff, J.-Y. L’Excellent, and J. Koster, “A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling,” SIAM J. Matrix Anal. & Appl., vol. 23, no. 1, pp. 15–41, Jan. 2001, doi: 10.1137/S0895479899358194.

    and

  • P. R. Amestoy, A. Buttari, J.-Y. L’Excellent, and T. Mary, “Performance and Scalability of the Block Low-Rank Multifrontal Factorization on Multicore Architectures,” ACM Trans. Math. Softw., vol. 45, no. 1, pp. 1–26, Mar. 2019, doi: 10.1145/3242094.

    → These references provide additional information about MUMPS as a solver option of Ipopt.

  • P. A. Gorry, “General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method,” Anal. Chem., vol. 62, no. 6, pp. 570–573, Jan. 1990, doi: 10.1021/ac00205a007.

    → This reference provides additional information about the Savitzky-Golay filter based on Gram Polynomials. The implementation is provided by the gram_savitzky_golay library (see Dependencies).

  • Sven R. Schepp, Jakob Thumm, Stefan B. Liu, and Matthias Althoff. 2022. SaRA: A Tool for Safe Human-Robot Coexistence and Collaboration through Reachability Analysis. In 2022 International Conference on Robotics and Automation (ICRA). IEEE, 4312–4317. https://doi.org/10.1109/ICRA46639.2022.9811952

    → This reference provides an approach to estimate possible occupancy regions of a human in a robot workspace. This approach is used to compare our human motion forecasting with uncertainties to a state-of-the-art approach (see Usage).

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