  Estimating the impact intensity is one of the significant tasks of the legged
robot. Accurate feedback of the impact may support the robot to plan a suitable
and efficient trajectory to adapt to unknown complex terrains. Ordinarily, this
task is performed by a force sensor in the robot's foot. In this letter, an
impact intensity estimation without using a force sensor is proposed. An
artificial neural network model is designed to predict the motor torques of the
legs in an instantaneous position in the trajectory without utilizing the
complex kinematic and dynamic models of motion. An unscented Kalman filter is
used during the trajectory to smooth and stabilize the measurement. Based on
the difference between the predicted information and the filtered value, the
state and intensity of the robot foot's impact with the obstacles are
estimated. The simulation and experiment on a quadruped robot are carried out
to verify the effectiveness of the proposed method.
