Continuous Dynamic Bipedal Jumping via Real-time Variable-model Optimization
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909321590210560 |
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| author | Li, Junheng Kolt, Omar Nguyen, Quan |
| author_facet | Li, Junheng Kolt, Omar Nguyen, Quan |
| contents | Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control. Real-time planning with full body dynamics over the entire jumping trajectory presents unsolved challenges in computation burden. In this paper, we propose a novel variable-model optimization approach, a unified framework of variable-model trajectory optimization (TO) and variable-frequency Model Predictive Control (MPC), to effectively realize continuous and robust jumping planning and control on HECTOR bipedal robot in real-time. The proposed TO fuses variable-fidelity dynamics modeling of bipedal jumping motion in different jumping phases to balance trajectory accuracy and real-time computation efficiency. In addition, conventional fixed-frequency control approaches suffer from unsynchronized sampling frequencies, leading to mismatched modeling resolutions. We address this by aligning the MPC sampling frequency with the variable-model TO trajectory resolutions across different phases. In hardware experiments, we have demonstrated robust and dynamic jumps covering a distance of up to 40 cm (57% of robot height). To verify the repeatability of this experiment, we run 53 jumping experiments and achieve 90% success rate. In continuous jumps, we demonstrate continuous bipedal jumping with terrain height perturbations (up to 5 cm) and discontinuities (up to 20 cm gap). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuous Dynamic Bipedal Jumping via Real-time Variable-model Optimization Li, Junheng Kolt, Omar Nguyen, Quan Robotics Systems and Control Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control. Real-time planning with full body dynamics over the entire jumping trajectory presents unsolved challenges in computation burden. In this paper, we propose a novel variable-model optimization approach, a unified framework of variable-model trajectory optimization (TO) and variable-frequency Model Predictive Control (MPC), to effectively realize continuous and robust jumping planning and control on HECTOR bipedal robot in real-time. The proposed TO fuses variable-fidelity dynamics modeling of bipedal jumping motion in different jumping phases to balance trajectory accuracy and real-time computation efficiency. In addition, conventional fixed-frequency control approaches suffer from unsynchronized sampling frequencies, leading to mismatched modeling resolutions. We address this by aligning the MPC sampling frequency with the variable-model TO trajectory resolutions across different phases. In hardware experiments, we have demonstrated robust and dynamic jumps covering a distance of up to 40 cm (57% of robot height). To verify the repeatability of this experiment, we run 53 jumping experiments and achieve 90% success rate. In continuous jumps, we demonstrate continuous bipedal jumping with terrain height perturbations (up to 5 cm) and discontinuities (up to 20 cm gap). |
| title | Continuous Dynamic Bipedal Jumping via Real-time Variable-model Optimization |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2404.11807 |