An adaptive vision system for guidance of a robotic manipulator to capture a tumbling satellite with unknown dynamics

This paper is focused on an adaptive vision system for the guidance of a robot to intercept a non-cooperative target satellite with unknown dynamics parameters. A Kalman filter is developed to reliably estimate the states of the object as well as all of its inertial parameters - namely, the moment-of-inertia ratios, the center-of-mass location, and the orientation of the principle axes - from vision information. The estimates are then used to optimally plan the motion of the manipulator. The optimization performance index includes the time of travel and the weighted norms of the end-effector velocity and acceleration, and it is subject to the conditions that the robot end-effector and the satellite gasping point arrive at the rendezvous point with the same velocity and that the interception occurs within the robot reach. The variational method is used to find the optimal path, which turns out to be the solution of a fourth-order differential equation. Subsequently, a closed-form solution is obtained. The solution to the optimal terminal-time problem is also obtained from the Hamiltonian of the entire system. Experiments are conducted by using a robotic arm to move a satellite mockup according to orbital mechanics and measuring the satellite pose by a laser camera system. The results demonstrate a successful grasping even though the inertial parameters are not known by the control system.