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Título: | Squash-box feasibility driven differential dynamic programming |
Autor: | Martí Saumell, Josep CSIC ORCID; Solà, Joan CSIC ; Mastalli, Carlos; Santamaria-Navarro, Àngel CSIC ORCID | Fecha de publicación: | 2020 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | IEEE/RSJ International Conference on Intelligent Robots and Systems: 7637-7644 (2020) | Resumen: | Recently, Differential Dynamic Programming (DDP) and other similar algorithms have become the solvers of choice when performing non-linear Model Predictive Control (nMPC) with modern robotic devices. The reason is that they have a lower computational cost per iteration when compared with off-the-shelf Non-Linear Programming (NLP) solvers, which enables its online operation. However, they cannot handle constraints, and are known to have poor convergence capabilities. In this paper, we propose a method to solve the optimal control problem with control bounds through a squashing function (i.e., a sigmoid, which is bounded by construction). It has been shown that a naive use of squashing functions damage the convergence rate. To tackle this, we first propose to add a quadratic barrier that avoids the difficulty of the plateau produced by the sigmoid. Second, we add an outer loop that adapts both the sigmoid and the barrier; it makes the optimal control problem with the squashing function converge to the original control-bounded problem. To validate our method, we present simulation results for different types of platforms including a multi-rotor, a biped, a quadruped and a humanoid robot. | Descripción: | Trabajo presentado en la IEEE/RSJ International Conference on Intelligent Robots and System, celebrada en Las Vegas (Estados Unidos), del 25 al 29 de octubre de 2020 | Versión del editor: | http://dx.doi.org/10.1109/IROS45743.2020.9340883 | URI: | http://hdl.handle.net/10261/235684 | DOI: | 10.1109/IROS45743.2020.9340883 | Identificadores: | doi: 10.1109/IROS45743.2020.9340883 issn: 2153-0858 |
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2352-Squash-box-feasibility-driven-differential-dynamic-programming.pdf | 1,72 MB | Adobe PDF | Visualizar/Abrir |
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