The planning of whole-body motion and step time for bipedal locomotion is constructed as a model predictive control (MPC) problem, in which a sequence of optimization problems needs to be solved online. While directly solving these problems is extremely time-consuming, we propose a predictive gait synthesizer to offer immediate solutions. Based on the full-dimensional model, a library of gaits with different speeds and periods is first constructed offline. Then the proposed gait synthesizer generates real-time gaits at 1kHz by synthesizing the gait library based on the online prediction of centroidal dynamics. We prove that the constructed MPC problem can ensure the uniform ultimate boundedness (UUB) of the CoM states and show that our proposed gait synthesizer can provide feasible solutions to the MPC optimization problems. Simulation and experimental results on a bipedal robot with 8 degrees of freedom (DoF) are provided to show the performance and robustness of this approach.
翻译:用于双肢移动的全体运动和足步时间规划被构建为一个模型预测控制(MPC)问题, 需要在线解决一系列优化问题。 虽然直接解决这些问题非常耗时, 我们提议了一个预测性步数合成器, 以提供即时解决方案。 基于全维模型, 首先在离线处建起一个速度和时数不同的音轨库。 然后, 提议的音量合成器在 1kHz 上生成实时音频, 其方法是根据对正态动态的在线预测对音频库进行合成。 我们证明, 构建的 MPC 问题可以确保COM 州的统一最终界限( UUB), 并显示我们提议的数位合成器可以为MPC 优化问题提供可行的解决方案。 提供了具有8度自由度的双体机器人的模拟和实验结果, 以显示这一方法的性能和稳健性 。