An innovate filter for space robots to unfirmly capture tumbling targets

In the postcapture stage, relative motion could exist between the target and the robot end-effector, which is called unfirm capture . Unmodeled and time-varying dynamic and kinematic coupling in unfirm capture cases obstructs the estimation of target inertia properties and relative motion states. To solve this problem, vision navigation information and general dynamic equations are combined to compensate unknown coupling dynamics based on the extended Kalman filter. To avoid disturbing force and torque reducing the precision of the state estimation, a recursive least square multiplicative extended Kalman filter is developed. By minimizing deviations between expected statistics and real statistics of process noise, this novel filter estimates parameters of the external disturbance. Simulations are carried out to demonstrate the efficiency and effectiveness of the proposed filter.