An Optimal Motion Planning Framework for Quadruped Jumping

Zhang, Song; Yue, Linzhu; Sun, Guoqiang; Ling, Yihu; Wei, Hongshuo; Gui, Linhai; Liu, Yunhui

doi:10.1109/iros47612.2022.9981642

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…1) Model-based optimal control for legged jumping: Prior model-based methods for legged jumping control usually build up a layered optimization scheme, which includes offline trajectory optimization with detailed models of the robot's dynamics and ground contacts [15], [19]- [21], and online controllers that leverage simplified models of the robot's dynamics [6], [22]- [24]. In order to optimize trajectories for jumping, which needs to switch among modes with different underlying dynamics, there are two commonly employed solutions: relying on human-tuned predefined contact sequences [5], [12], [14], [25], [26], which is not scalable to different jump distances and/or directions, or leveraging contact-implicit optimization [3], [27]- [30] which plans through contacts to avoid breaking the trajectory or using computationally expensive mixed-integer programming [20], [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning

Li¹,

Peng²,

Abbeel³

et al. 2023

Preprint

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0°-55°( a) (b) (c) Flight Phase Fig. 1: Representative dynamic jumping maneuvers performed by a bipedal robot Cassie using the proposed multi-task control policies. From left to right: (a) the robot jumps over 1.4 m and lands at the given target; (b) the robot jumps to a target that is 0.88 m in front of the robot and 0.44 m above the ground, and (c) the robot jumps in place while turning 55 • with a command to turn 60 • in place. The policies are trained in simulation and deployed on the hardware without further tuning. Video is at: https://youtu.be/aAPSZ2QFB-E.

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Section: Related Workmentioning

confidence: 99%

Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning

Li¹,

Peng²,

Abbeel³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…1) Model-based optimal control for legged jumping: Prior model-based methods for legged jumping control usually build up a layered optimization scheme, which includes offline trajectory optimization with detailed models of the robot's dynamics and ground contacts [10,12,46,63], and online controllers that leverage simplified models of the robot's dynamics [45,55,65,72]. In order to optimize trajectories for jumping, which needs to switch among modes with different underlying dynamics, there are two commonly employed solutions: (i) relying on human-specified contact sequences [9,19,29,47,71], which is not scalable to different jump distances and/or directions, or (ii) leveraging contactimplicit optimization [8,14,33,52,77] which plans through contacts to avoid breaking the trajectory or using computationally expensive mixed-integer programming [1,11,12].…”

Section: Related Workmentioning

confidence: 99%

Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

Li,

Peng,

Abbeel

et al. 2023

Robotics: Science and Systems XIX

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“…The demand for adaptation to complex environments presents challenges for both the stability and agility of quadruped robots. Highly dynamic jumping can deal with a variety of complex environments to improve quadruped robots’ agility [ 1 , 2 , 3 , 4 , 5 ]. Jumping behavior requires high speeds to be attained in a short time, which is associated with challenges related to physical constraints such as joint configuration and the friction cone.…”

Section: Introductionmentioning

confidence: 99%

Research on the Jumping Control Methods of a Quadruped Robot That Imitates Animals

et al. 2023

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At present, most quadruped robots can move quickly and steadily on both flat and undulating ground; however, natural environments are complex and changeable, so it is important for a quadruped robot to be able to jump over obstacles immediately. Inspired by the jumping movement of quadruped animals, we present aerial body posture adjustment laws and generate animal-like jumping trajectories for a quadruped robot. Then, the bionic reference trajectories are optimized to build a trajectory library of a variety of jumping motions based on the kinematic and dynamic constraints of the quadruped robot. The model predictive control (MPC) method is employed by the quadruped robot to track the optimized trajectory to achieve jumping behavior. The simulations show that the quadruped robot can jump over an obstacle of 40 cm in height. The effectiveness of the animal-like jump control method is verified.

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An Optimal Motion Planning Framework for Quadruped Jumping

Cited by 10 publications

References 19 publications

Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning

Robust and Versatile Bipedal Jumping Control through Multi-Task Reinforcement Learning

Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

Research on the Jumping Control Methods of a Quadruped Robot That Imitates Animals

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