Robust Estimation of Motion States for Free-Floating Tumbling Target Capture

In this paper, we propose a novel extended Kalman filter (EKF)to aid the capture of a free-floating tumbling satellite (Target)with a manipulator-equipped spacecraft (Servicer)in the close-range capture phase. For such a control problem, the interfacing of a fast-sampled robot controller with slow-sampled exteroceptive sensors on the spacecraft causes a performance loss in the robot controller. In order to circumvent this problem, a method is proposed with the main objective of providing fast relative state reconstruction between Target and Servicer. To this end, the proposed EKF estimates the inertial motion states of the Target and the base of the Servicer at a high rate using slow-sampled and noisy exteroceptive measurements, which include relative poses from a camera and a Light Detection And Ranging sensor (LiDAR)and absolute orientation from star/sun trackers. The inherent sensor redundancy provides robustness during sensor occlusion. The state information is combined with the measurements from Inertial Measurement Unit (IMU), forward kinematics (using robot joint encoders)and a priori known transformations to provide fast-sampled estimates of the inertial states. This information is used to reconstruct relative states for feedback control, and furthermore, the Target's tumbling velocity is also estimated for feed-forward. The novelty of the EKF is in the simultaneous estimation of two quaternion states from a composite measurement of both. The robustness of the proposed EKF against parametric uncertainty was validated with 640 Monte-Carlo simulations, a summary of which is presented. Furthermore, the validity of the EKF is demonstrated by using it in closed-loop with a combined controller on Guidance, Navigation and Control Development Environment (GNCDE)software.