面向高精度里程估计的2轮驱动移动机器人运动控制优化方法OA

To address issues such as significant odometry drift,non-smooth motion response,accumulated system errors,and unstable Inertial Measurement Unit(IMU)bias in two-wheeled drive mobile robots,the research team proposed a comprehensive optimization strategy.A motion-state-aware Adaptive Extended Kalman Filter(AEKF)was designed,dynamically adjusting observation weights based on residuals between encoders and IMU to enhance state estimation robustness;An S-curve acceleration and deceleration profile with feedforward compensation,tailored to servo dynamic characteristics and constrained by current limits,was developed to generate smooth motion commands;An online error compensation mechanism based on round-trip task consistency was constructed to self-calibrate wheelbase and wheel diameter deviations;A multi-source stationary detection fusion method was developed for IMU bias correction,significantly improving long-term heading stability.Experimental results demonstrate that the proposed strategy substantially enhances odometry accuracy and system stability,validating its engineering practicality and robustness.