A Fast Online Omnidirectional Quadrupedal Jumping Framework Via Virtual-Model Control and Minimum Jerk Trajectory Generation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913410141126656 |
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| author | Yue, Linzhu Zhang, Lingwei Song, Zhitao Zhang, Hongbo Dong, Jinhu Zeng, Xuanqi Liu, Yun-Hui |
| author_facet | Yue, Linzhu Zhang, Lingwei Song, Zhitao Zhang, Hongbo Dong, Jinhu Zeng, Xuanqi Liu, Yun-Hui |
| contents | Exploring the limits of quadruped robot agility, particularly in the context of rapid and real-time planning and execution of omnidirectional jump trajectories, presents significant challenges due to the complex dynamics involved, especially when considering significant impulse contacts. This paper introduces a new framework to enable fast, omnidirectional jumping capabilities for quadruped robots. Utilizing minimum jerk technology, the proposed framework efficiently generates jump trajectories that exploit its analytical solutions, ensuring numerical stability and dynamic compatibility with minimal computational resources. The virtual model control is employed to formulate a Quadratic Programming (QP) optimization problem to accurately track the Center of Mass (CoM) trajectories during the jump phase. The whole-body control strategies facilitate precise and compliant landing motion. Moreover, the different jumping phase is triggered by time-schedule. The framework's efficacy is demonstrated through its implementation on an enhanced version of the open-source Mini Cheetah robot. Omnidirectional jumps-including forward, backward, and other directional-were successfully executed, showcasing the robot's capability to perform rapid and consecutive jumps with an average trajectory generation and tracking solution time of merely 50 microseconds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00658 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Fast Online Omnidirectional Quadrupedal Jumping Framework Via Virtual-Model Control and Minimum Jerk Trajectory Generation Yue, Linzhu Zhang, Lingwei Song, Zhitao Zhang, Hongbo Dong, Jinhu Zeng, Xuanqi Liu, Yun-Hui Robotics 68T40 I.2.9 Exploring the limits of quadruped robot agility, particularly in the context of rapid and real-time planning and execution of omnidirectional jump trajectories, presents significant challenges due to the complex dynamics involved, especially when considering significant impulse contacts. This paper introduces a new framework to enable fast, omnidirectional jumping capabilities for quadruped robots. Utilizing minimum jerk technology, the proposed framework efficiently generates jump trajectories that exploit its analytical solutions, ensuring numerical stability and dynamic compatibility with minimal computational resources. The virtual model control is employed to formulate a Quadratic Programming (QP) optimization problem to accurately track the Center of Mass (CoM) trajectories during the jump phase. The whole-body control strategies facilitate precise and compliant landing motion. Moreover, the different jumping phase is triggered by time-schedule. The framework's efficacy is demonstrated through its implementation on an enhanced version of the open-source Mini Cheetah robot. Omnidirectional jumps-including forward, backward, and other directional-were successfully executed, showcasing the robot's capability to perform rapid and consecutive jumps with an average trajectory generation and tracking solution time of merely 50 microseconds. |
| title | A Fast Online Omnidirectional Quadrupedal Jumping Framework Via Virtual-Model Control and Minimum Jerk Trajectory Generation |
| topic | Robotics 68T40 I.2.9 |
| url | https://arxiv.org/abs/2407.00658 |