Xie Sicheng, Li Xinyu, Gao Liang, Fu Ling, Jing Li, Xu Weifeng
State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
ISA Trans. 2023 Apr;135:410-427. doi: 10.1016/j.isatra.2022.10.012. Epub 2022 Oct 23.
Environmental adaptability and real-time control are significant to the actual application of biped robots. The current Spring-Loaded Inverted Pendulum (SLIP) walking exhibits the compliant interaction with environments. However, the movability and controllability of this model is limited owing to the lack of ankles. Moreover, complicated nonlinear optimization problems in gait generation bring difficulties to real-time control. To overcome these problems, this study proposes an online whole-stage gait planning method to enhance the bipedal walking performance. Firstly, considering the role of ankles, this study applies the proposed template model called Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (VSLIP-FF) model. Then a Finite State Machine (FSM)-based gait pattern including the corresponding bio-inspired gait strategies is established, which extends the single cyclic gait to the whole-stage gait. Secondly, to realize real-time gait planning, an online gait generator based on a neural network is applied to reduce the calculational burden. Finally, the method is applied on the simulation prototype and real robot platform for verification. Experimental results validate that the proposed method can achieve an autonomous gait with the online planning time of 0.01s, and the step length range is expanded by 37.52% compared with the traditional SLIP model.
环境适应性和实时控制对于双足机器人的实际应用具有重要意义。当前的弹簧加载倒立摆（SLIP）行走方式展现出与环境的柔顺交互。然而，由于缺少脚踝，该模型的可移动性和可控性受到限制。此外，步态生成中复杂的非线性优化问题给实时控制带来困难。为克服这些问题，本研究提出一种在线全阶段步态规划方法以提升双足行走性能。首先，考虑到脚踝的作用，本研究应用所提出的名为有限尺寸足部可变弹簧加载倒立摆（VSLIP-FF）模型的模板模型。然后建立基于有限状态机（FSM）的步态模式，包括相应的仿生步态策略，将单周期步态扩展为全阶段步态。其次，为实现实时步态规划，应用基于神经网络的在线步态生成器以减轻计算负担。最后，将该方法应用于仿真原型和真实机器人平台进行验证。实验结果验证了所提方法能够实现自主步态，在线规划时间为0.01秒，与传统SLIP模型相比步长范围扩大了37.52%。
