Zuo Weilong, Gao Junyao, Liu Jiongnan, Wu Taiping, Xin Xilong
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Beijing Advanced Innovation Center for Intelligent Robotics and Systems, Beijing Institute of Technology, Beijing 100081, China.
Biomimetics (Basel). 2024 Apr 19;9(4):245. doi: 10.3390/biomimetics9040245.
When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.
当人形机器人在人类环境中工作时，它们容易摔倒。然而，当周围有可利用的物体时，人形机器人可以利用它们来实现平衡。为了解决这个问题，本文使用可变高度倒立摆模型建立了机器人的状态方程，并采用模型预测控制实现了在线轨迹优化。对于手臂运动过程中的最佳关节角度，本文采用分布式多边形方法来计算手臂姿态。为确保机器人在运动过程中平稳快速地到达目标位置，本文采用全身运动控制方法，建立了机器人运动多目标约束的代价函数。这些约束包括全身动力学、质心约束、手臂末端执行器约束、摩擦约束和压力中心约束。在仿真中，比较了四组方法，实验结果表明，与自由落体运动相比，采用本文提出的方法可将机器人触墙时的最大加速度降低到69.1m/s，有效降低了着陆时的冲击力。最后，在实际实验中，我们将机器人放置在离墙0.85m处并施加向前的推力。我们观察到机器人能够稳定地着陆在墙上，并且冲击力在机器人可接受的范围内，证实了所提方法的实际有效性。
