Perceptive Locomotion through Nonlinear Model Predictive Control
| Autor: | Ruben Grandia, Fabian Jenelten, Shaohui Yang, Farbod Farshidian, Marco Hutter |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
motion generation
FOS: Computer and information sciences rough-terrain algorithm ANYmal iteration scheme RSL Perceptive Locomotion dynamic locomotion Computer Science Applications optimal control Computer Science - Robotics framework Control and Systems Engineering terrain perception Electrical and Electronic Engineering legged locomotion trajectory optimization implementation Robotics (cs.RO) |
| Zdroj: | IEEE Transactions on Robotics |
| ISSN: | 1552-3098 1042-296X 1941-0468 |
| DOI: | 10.3929/ethz-b-000564782 |
| Popis: | Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, that can optimize motions for all degrees of freedom of the robot in real-time. To mitigate the numerical challenges posed by the terrain, a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multipleshooting, real-time iteration, and a filter-based line search are used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing. IEEE Transactions on Robotics ISSN:1552-3098 ISSN:1042-296X ISSN:1941-0468 |
| Databáze: | OpenAIRE |
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