提出三步优化框架，让人形机器人实现敏捷前跳。
A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion
- 将机器人建模为静态反向动量摆，优化质心与支撑点轨迹
- 通过二次规划求解器将轨迹映射到关节空间，实现高效迭代
- 在仿真与实验中完成1米远、0.5米高的前跳，验证可行性
高动态跳跃是人形机器人实现环境适应和越障的重要挑战。轨迹优化是实现高动态、爆发式跳跃的有效方法。本文提出一种三步轨迹优化框架，用于生成人形机器人的跳跃动作。第一阶段将机器人视为静态反向动量摆（SRMP）模型，结合动量、惯性和重心压力点（CoP）生成基础轨迹；第二阶段利用高效的二次规划（QP）求解器将轨迹映射至关节空间；第三阶段基于前两阶段结果生成全身关节轨迹。通过综合考虑动量与惯性，机器人实现了敏捷的前向跳跃。论文展示了仿真与实验结果，成功完成了距离1.0米、高度0.5米的前向跳跃，验证了该框架的适用性。
原文摘要 · Abstract (English)
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 incorporates 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. \ref{Fig First page fig}) 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.
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