Online Adaptive Motion Generation for Humanoid Locomotion on Non-Flat Terrain via Template Behavior Extension

作者：Xiang Meng, Zhangguo Yu, Xuechao Chen, Zelin Huang, Fei Meng, Qiang Huang · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2023 · DOI：10.1109/tase.2023.3327819 · 被引用次数：14 · 研究领域：Robotic Locomotion and Control、Prosthetics and Rehabilitation Robotics、Neurogenetic and Muscular Disorders Research

For humanoid robots, online motion generation on non-flat terrain remains an ongoing research challenge. Computational complexity is one of the primary restrictions that preclude motion planners from generating adaptive behaviors online. In this paper, we investigate this problem and decompose it into two sequential components: an Efficient Behavior Generator (EBG) and a Nonlinear Centroidal Model Predictive Controller (NC-MPC). The EBG is responsible for optimizing the physically feasible whole-body template behaviors, which can provide reliable warm-starts for NC-MPC, thereby greatly reducing the computational effort of online planning. With tailored objective function and feet complementary constraints, the EBG can search for a near-optimal solution after several iterations within seconds for different behaviors including walking, running, and jumping, even with intuitive initial guesses. To make the template behaviors extensible when the robot encounters possible different scenarios, the NC-MPC is proposed to regenerate the reactive motion online to adapt it to the real local environment. Finally, we validate the effectiveness of synthesizing EBG and NC-MPC for humanoid locomotion on non-flat terrain in simulation and on the real humanoid robot BHR7P.Note to Practitioners— For current humanoid robots, dynamically traversing non-flat terrain such as stairs, slopes, and gaps in the real world presents a significant challenge. In this paper, we propose an adaptive motion pla...