A Multi-Modal Fusion Framework for State Estimation in Four-Wheel-Legged Robots

The wheel-legged composite robot combines the advantages of efficient wheeled movement and legged obstacle crossing, but its state estimation accuracy is limited by sensors dependent on contact detection and the challenge of multi-source data fusion. Aiming at the four-legged wheel-legged robot, we proposed a contact estimator based on multi-probability model fusion and a position and velocity estimator based on odometry to achieve high-precision state estimation with low hardware cost. The contact estimator constructed a three-layer probability model: the gait planning model dynamically estimated the contact probability based on phase information, the knee flexion and extension joint torque model used the mutation characteristics of joint torque to establish a Gaussian distribution mapping, the wheel bottom height model constructed a probability relationship through the geometric constraint of ground clearance height, and then fused the three-layer output through Kalman filter to solve the problems of high cost and difficult wiring of traditional sensors. The position and velocity estimator fused motor encoder and IMU data, constructs Kalman filter state equation and observation equation, calculated the pose state of the wheel bottom and the centroid in the world coordinate system, and improved the estimation efficiency under nonholonomic constraints. Simulation results showed that the contact state discrimination results of the contact estimator in pure rolling and mixed motion are highly consistent with the real contact state, and the estimation results of the position and velocity estimator in the X, Y and Z directions are in good agreement with the real values, and the estimation error is always kept within a small range. This method provides reliable state feedback for the dynamic control of the wheel-legged robot.