Research On Balance Control And Gait Planning Of Biped Robot

Driving technology,artificial intelligence,high-performance computers and other latest technology has enable bipedal robots to roughly emulate the motor dexterity of humans,able to dance show,musical instruments,and talking.However,this ability still have big gap between putting into practical application.Mainly reflected in the lack of the ability of balance,and the coordination of walking.High demands on the working environment,poor adaptability in unstructured environments.In this paper,the self-developed bipedal humanoid robot is researched,and the balance control,impedance control and gait planning are mainly studied.This paper first introduces the hardware and software architecture of the biped robot,and establishes the ADAMS and Gazebo simulation to assist in the prediction and optimization of the performance of the control algorithm,so as to reduce the risk operation of the physical robot and avoiding the potential risks.Then the forward kinematics and inverse kinematics of the biped robot are analyzed and the kinematic library KDL is introduced to simplify the kinematic operation.Stable balance control is still a challenge for biped robots.In this paper,we present two schemes for impedance adjustment when dealing with the balance.One is the fixed impedance model,which is simple and effective,but there is a problem of vibration,a filter is combined in this paper to improve the balance control effect.The other is an adaptive impedance model based on integral reinforcement learning.This method can obtain the optimal solution online by using the policy iteration without knowing the dynamic information of the system.It is a further optimization of the LQR method.Then the scheme is simulated and experimented,and the advantages and disadvantages are analyzed.Gait planning is the most basic part of robot motion control.First,a simplified five-link planar robot model is established to facilitate the study.Then,the ZMP-based polynomial fitting method is used to realize the gait planning of the robot’s horizontal walking.Then the dynamic model is analyzed and the PD controller is used to simulate the motion.A new strategy of PD and RBF neural network hybrid control is proposed to reduce the tracking error during DSP.Again,the simulation results show that the scheme can reduce the tracking error.Finally,this paper applies the polynomial fitting method to carry on the static walking and the stairway gait planning of the physical robot of the experimental platform.In view of the particularity of the gait planning of the stairs,this paper proposes a partition fitting to realize the cooperative planning of each joint and introduces the trunk leaning forward to assist the body balance.Due to time constraints,this paper has achieved a stable walking experiment of bipedal robots,and the stair experiment is still lacking in robustness,which will be the next step of the work.