Abstract
Biped robot research has always been a research focus in the field of robot research. Among them, the motion control system, as the core content of the biped robot research, directly determines the stability of the robot walking. Traditional biped robot control methods suffer from low model accuracy, poor dynamic characteristics of motion controllers, and poor motion robustness. In order to improve the walking robustness of the biped robot, this paper solves the problem from three aspects: planning method, mathematical model, and control method, forming a robot motion control framework based on the whole-body dynamics model and quadratic planning. The robot uses divergent component of motion for trajectory planning and introduces the friction cone contact model into the control frame to improve the accuracy of the model. A complete constraint equation system can ensure that the solution of the controller meets the dynamic characteristics of the biped robot. An optimal controller is designed based on the control framework, and starting from the Lyapunov function, the convergence of the optimal controller is proved. Finally, the experimental results show that the method is robust and has certain anti-interference ability.
Highlights
Humanoid robots are expected to perform various tasks in the near future
Arm movement was originally used for dynamic balance during walking, but it can be used to assist in tasks such as kicking [1]. erefore, a full-body motion control framework needs to be established to find the best control output, so that the robot can complete various expected tasks under the constraint conditions such as maintaining balance [2, 3]
It should be noted that even in the case where the upperlayer trajectory is based on a simplified model, since the lower layer considers a more complex model, this makes the resulting joint trajectory conform to the dynamic characteristics of the actual robot and can follow the upperlayer trajectory very well
Summary
Humanoid robots are expected to perform various tasks in the near future. Humanoid robots have the characteristics of multiple joints and multiple degrees of freedom, which makes them have the potential to satisfy multitasking. e humanoid robot’s motion control often faces some difficult situations, such as the number of degrees of freedom required by the robot to perform a variety of expected tasks is higher than the number of degrees of freedom the robot has. For a legged robot with six underactuated degrees of freedom as a whole, the most important tasks of motion control are maintaining balance and completing the expected trajectory. E upperlevel motion plan usually uses a simplified dynamic model to generate the center of gravity and foot trajectory, while the lower level below considers using a more complex model to generate the position or torque of each joint. It should be noted that even in the case where the upperlayer trajectory is based on a simplified model, since the lower layer considers a more complex model, this makes the resulting joint trajectory conform to the dynamic characteristics of the actual robot and can follow the upperlayer trajectory very well. Is paper proposes a whole-body motion control method based on inverse dynamics, integrating motion control into an optimization framework based on quadratic programming and using joint torque and linear contact force as optimization variables for quadratic programming problems.
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