Bio-inspired optimal control framework to generate walking motions for the humanoid robot iCub using whole body models
Bipedal locomotion remains one of the major open challenges of humanoid robotics. The common approaches are based on simple reduced model dynamics to generate walking trajectories, often neglecting the whole-body dynamics of the robots. As motions in nature are often considered as optimal with respe...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
12 February 2018
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| In: |
Applied Sciences
Year: 2018, Volume: 8, Issue: 2 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app8020278 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3390/app8020278 Verlag, kostenfrei, Volltext: http://www.mdpi.com/2076-3417/8/2/278 
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| Author Notes: | Yue Hu and Katja Mombaur |
| Summary: | Bipedal locomotion remains one of the major open challenges of humanoid robotics. The common approaches are based on simple reduced model dynamics to generate walking trajectories, often neglecting the whole-body dynamics of the robots. As motions in nature are often considered as optimal with respect to certain criteria, in this work, we present an optimal control-based approach that allows us to generate optimized walking motions using a precise whole-body dynamic model of the robot, in contrast with the common approaches. The optimal control problem is formulated to minimize a set of desired objective functions with respect to physical constraints of the robot and contact constraints of the walking phases; the problem is then solved with a direct multiple shooting method. We apply the formulation with combinations of different objective criteria to the model of a reduced version of the iCub humanoid robot of 15 internal DOF. The obtained trajectories are executed on the real robot, and we carry out a discussion on the differences between the outcomes of this approach with the classic approaches. |
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| Item Description: | Gesehen am 18.04.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app8020278 |