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.
In recent years humanoid robots have been widely used in toy, performance, education and other service industries, but most biped robots walk slowly and have poor stability. The reason is that the driver parameters of the robot cannot properly match the walking gait algorithm, and the insufficient performance of the robot driver leads to the poor motion capability of the robot. In this paper, the optimization design process of biped robot parameters is studied and expounded, and its motion capability is improved by optimizing the driving parameters of the robot. Firstly, the contradiction between walking speed, stability and driver performance of biped robot is analysed. The performance evaluation functions of the three are further established, and the optimal parameter design to a certain extent is realized based on the multi-objective optimization method. Finally, combining with the physical simulation engine, the design parameters are simulated and checked, and the robot design process is completed through the guidance of simulation results.
In order to walk in a physical environment, the biped will encounter various external disturbances, and walking under persistent conditions is still challenging. This paper tries to improve the push recovery performance based on capture point (CP) and model predictive control. The trajectory of zero moment point (ZMP) and center of mass are solved and predicted in a limited time horizon. Online footprint generator is combined with MPC walking pattern generation, which can keep biped stable in the next few steps, and projection of ZMP is used to calculate the next footprint and reach the target CP in an incremental way. Verification of the proposed stable biped walking method is conducted by simulation and experiments.
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