基于IMU和运动学的四足机器人对角支撑状态估计算法

State estimation for diagonal support of quadruped robot based on IMUand kinematics

对角支撑是四足机器人复杂动态以及线性行走必备的一种状态.然而,目前鲜有有效的状态估计算法,无法支撑相应控制策略.为此,提出一种融合惯性测量单元(inertial measurement unit, IMU)和运动学约束的状态估计算法.首先,针对对角支撑状态下运动学模型欠约束带来的位姿估计难题,通过分解机身坐标系到参考坐标系的变换矩阵,简化运动学方程,并利用IMU提供的可信俯仰和横滚角测量值,推导获得其余4个位姿量的解析式;然后,进一步基于腿部微分运动学和IMU提供的俯仰和横滚角速率,结合位姿量的导数获得线速度和偏航角速率的解析式;最后,通过仿真分析验证所提出算法的有效性.实验结果表明:位姿估计值准确、无漂移,偏航角速率和线速度的均方根误差分别小于2 deg/s和9 mm/s,优于现有算法且满足实际控制需求.

Diagonal support is necessary for complex dynamic gait and linear walking of quadruped robots.However,no state estimation method currently supports the corresponding control strategy.In this paper,a state estimation algorithm that fuses inertial measurement units(IMUs)and kinematic constraints is proposed.Firstly,aiming at the pose estimation problem caused by under-constrained kinematics of diagonal support,the kinematic equations are simplified by decomposing the transformation matrix from the body coordinate system to the reference coordinate system,and the analytical formulas of the remaining four pose states are obtained by using the credible pitch and roll measurements of the IMU.Then,based on the differential kinematics of the leg and the pitch and roll angular rates of the IMU,the analytical formulas of the linear velocity and the yaw angular rate are obtained by combining the derivative of pose.Simulation analysis verifies the effectiveness of the proposed algorithm.The experimental results show that the pose estimation values are accurate and drift-free,and the root mean square errors of yaw angular rate and linear velocity are less than 2 deg/s and 9 mm/s respectively,which outperform existing algorithms and meet the practical control requirements.