A Three-Step Optimization Framework With Hybrid Models for a Humanoid Robot’s Jump Motion

作者：Haoxiang Qi, Zhangguo Yu, Xuechao Chen, Qingqing Li, Yaliang Liu, Chuanku Yi, Chencheng Dong, Fei Meng, Qiang Huang · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2025 · DOI：10.1109/tase.2025.3557162 · 被引用次数：4 · 研究领域：Robotic Locomotion and Control、Robotic Path Planning Algorithms、Robotic Mechanisms and Dynamics

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization in-corporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework.