用四足机器人在窄路行走时通过推进器稳定身体姿态。
Quadratic Programming-Based Posture Manipulation and Thrust-vectoring for Agile Dynamic Walking on Narrow Pathways
- 基于质心动力学模型，用二次规划优化控制推进器与足部受力。
- 在模拟中实现窄路径稳定行走，抗侧向推力扰动能力提升。
- 适合研究复杂地形动态行走与多模态机器人的研究者。
本文针对四足-飞行混合平台Husky β在窄路径上的敏捷动态行走问题，提出一种基于质心动力学模型的控制方法。该机器人在每个矢状面膝关节配备推进器，可主动调节前向动态稳定性。控制器以二次规划（QP）求解器为基础，在模型预测控制框架下联合优化推进器推力与足地接触力。仿真结果显示，该系统可在窄路径上实现稳定行走；同时，在横向推力扰动恢复测试中，推进器有效提升了机体的侧面稳定性。研究验证了推进器辅助行走对增强运动灵活性和鲁棒性的可行性。
原文摘要 · Abstract (English)
There has been significant advancement in legged robot's agility where they can show impressive acrobatic maneuvers, such as parkour. These maneuvers rely heavily on posture manipulation. To expand the stability and locomotion plasticity, we use the multi-modal ability in our legged-aerial platform, the Husky Beta, to perform thruster-assisted walking. This robot has thrusters on each of its sagittal knee joints which can be used to stabilize its frontal dynamic as it walks. In this work, we perform a simulation study of quadruped narrow-path walking with Husky $β$, where the robot will utilize its thrusters to stably walk on a narrow path. The controller is designed based on a centroidal dynamics model with thruster and foot ground contact forces as inputs. These inputs are regulated using a QP solver to be used in a model predictive control framework. In addition to narrow-path walking, we also perform a lateral push-recovery simulation to study how the thrusters can be used to stabilize the frontal dynamics.
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