提出新型柔性四足模型，实现稳定高效的跳跃与爆发性运动。
Versatile, Robust, and Explosive Locomotion with Rigid and Articulated Compliant Quadrupeds
- 用解耦弹簧与驱动的简化模型模拟并联柔顺性，提升控制精度。
- 实测跳跃距离、转向角度和抗扰能力提升至少25%、15%和100%。
- 适用于需要高动态性能的机器人场景，如复杂地形快速移动。
在动态不确定性下实现灵活且爆发性强的运动是极具挑战的任务。引入并联柔顺性可提升四足机器人的运动性能，但会增加控制难度。本文提出一种通用模板模型，并建立高效运动规划与控制流程。首先，设计了一种简化模型——带躯干旋转的双足主动弹簧倒立摆，通过解耦弹簧效应与主动驱动，显式建模并联柔顺性。基于该模型，采用双层轨迹优化生成多种灵巧动作，如跳跃（pronking）、蛙跳（froggy jumping）和转跳（hop-turn），并结合无奇点的体旋转表示。集成线性无奇点跟踪控制器后，显著提升了四足运动性能。与现有模型对比，本模型精度更高、泛化能力更强。硬件实验在刚性四足与新设计的柔性四足上验证：i）模板模型可生成多样化动态运动；ii）并联弹性使爆发性运动增强，例如最大跳跃距离、转跳偏航角和蛙跳距离分别提升至少25%、15%和25%；iii）并联弹性提高对动态不确定性的鲁棒性，如允许支撑面高度变化范围扩大100%，实现更稳定的蛙跳。
原文摘要 · Abstract (English)
Achieving versatile and explosive motion with robustness against dynamic uncertainties is a challenging task. Introducing parallel compliance in quadrupedal design is deemed to enhance locomotion performance, which, however, makes the control task even harder. This work aims to address this challenge by proposing a general template model and establishing an efficient motion planning and control pipeline. To start, we propose a reduced-order template model-the dual-legged actuated spring-loaded inverted pendulum with trunk rotation-which explicitly models parallel compliance by decoupling spring effects from active motor actuation. With this template model, versatile acrobatic motions, such as pronking, froggy jumping, and hop-turn, are generated by a dual-layer trajectory optimization, where the singularity-free body rotation representation is taken into consideration. Integrated with a linear singularity-free tracking controller, enhanced quadrupedal locomotion is achieved. Comparisons with the existing template model reveal the improved accuracy and generalization of our model. Hardware experiments with a rigid quadruped and a newly designed compliant quadruped demonstrate that i) the template model enables generating versatile dynamic motion; ii) parallel elasticity enhances explosive motion. For example, the maximal pronking distance, hop-turn yaw angle, and froggy jumping distance increase at least by 25%, 15% and 25%, respectively; iii) parallel elasticity improves the robustness against dynamic uncertainties, including modelling errors and external disturbances. For example, the allowable support surface height variation increases by 100% for robust froggy jumping.
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