In order to smoothly perform interactions between a humanoid robot and a human, knowledge about the human locomotion can be efficiently used. Indeed, in a human-robot collaboration, a prediction model of the human behaviour allows the robot to act proactively. In this letter, an optimal control based model predicting the human Center of Mass (CoM) trajectory during gait is presented. A Walking Pattern Generator (WPG) based on non-linear model predictive control is, then, introduced in order to generate the robot CoM and footsteps along the predicted trajectory. The combination of the human trajectory prediction model and this new WPG aims to allow the robot to proactively walk along with a human instead of passively follow him. These models have been tested in simulation on Gazebo on a TALOS humanoid robot model using measured human trajectories. To perform the CoM and foot trajectories computed by the WPG, a real-time whole-body controller is used. This controller is a Quadratic Program which solves the inverse dynamics of the robot at torque level.

中文翻译：

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人体轨迹预测模型及其与仿人机器人行走模式生成器的耦合


为了顺利地执行人形机器人和人类之间的交互，可以有效地使用有关人类运动的知识。事实上，在人机协作中，人类行为的预测模型可以让机器人主动采取行动。在这封信中，提出了一种基于最优控制的模型，可预测步态期间的人体质心 (CoM) 轨迹。然后，引入基于非线性模型预测控制的行走模式生成器（WPG），以生成机器人 CoM 和沿着预测轨迹的脚步。人类轨迹预测模型与这种新的 WPG 的结合旨在让机器人主动地与人类一起行走，而不是被动地跟随人类。这些模型已使用测量的人体轨迹在 TALOS 人形机器人模型上的 Gazebo 上进行了模拟测试。为了执行由 WPG 计算的 CoM 和足部轨迹，使用了实时全身控制器。该控制器是一个二次程序，可求解机器人在扭矩水平下的逆动力学问题。