This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM. The RL model integrates collision risk estimates, satellite specifications, and debris data to generate an optimal maneuver matrix for OOS rendezvous and collision prevention. We employ the Cross-Entropy algorithm to find optimal decision policies efficiently. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. However, merging spacecraft rendezvous and optimal CAM presents significant complexities. We discuss design challenges and critical parameters for the successful implementation of the framework presented through a case study.

This paper introduces an innovative guidance and control method for simultaneously capturing and stabilizing a fast-spinning target satellite, such as a spin-stabilized satellite, using a spinning-base servicing satellite equipped with a robotic manipulator, joint locks, and reaction wheels (RWs). The method involves controlling the RWs of the servicing satellite to replicate the spinning motion of the target satellite, while locking the manipulator's joints to achieve spin-matching. This maneuver makes the target stationary with respect to the rotating frame of the servicing satellite located at its center-of-mass (CoM), simplifying the robot capture trajectory planning and eliminating post-capture trajectory planning entirely. In the next phase, the joints are unlocked, and a coordination controller drives the robotic manipulator to capture the target satellite while maintaining zero relative rotation between the servicing and target satellites. The spin stabilization phase begins after completing the capture phase, where the joints are locked to form a single tumbling rigid body consisting of the rigidly connected servicing and target satellites. An optimal controller applies negative control torques to the RWs to dampen out the tumbling motion of the interconnected satellites as quickly as possible, subject to the actuation torque limit of the RWs and the maximum torque exerted by the manipulator's end-effector.

This paper presents a method for guiding a robot manipulator to capture and bring a tumbling satellite to a state of rest. The proposed approach includes developing a coordination control for the combined system of the space robot and the target satellite, where the satellite acts as the manipulator payload. This control ensures that the robot tracks the optimal path while regulating the attitude of the chase vehicle to a desired value. Two optimal trajectories are then designed for the pre- and post-capture phases. In the pre-capturing phase, the manipulator manoeuvres are optimized by minimizing a cost function that includes the time of travel and the weighted norms of the end-effector velocity and acceleration, subject to the constraint that the robot end-effector and a grapple fixture on the satellite arrive at the rendezvous point with the same velocity. In the post-grasping phase, the manipulator dumps the initial velocity of the tumbling satellite in minimum time while ensuring that the magnitude of the torque applied to the satellite remains below a safe value. Overall, this method offers a promising solution for effectively capturing and bringing tumbling satellites to a state of rest.

Space debris is on the rise due to the increasing demand for spacecraft for com-munication, navigation, and other applications. The Space Surveillance Network (SSN) tracks over 27,000 large pieces of debris and estimates the number of small, un-trackable fragments at over 1,00,000. To control the growth of debris, the for-mation of further debris must be reduced. Some solutions include deorbiting larger non-cooperative resident space objects (RSOs) or servicing satellites in or-bit. Both require rendezvous with RSOs, and the scale of the problem calls for autonomous missions. This paper introduces the Multipurpose Autonomous Ren-dezvous Vision-Integrated Navigation system (MARVIN) developed and tested at the ORION Facility at Florida Institution of Technology. MARVIN consists of two sub-systems: a machine vision-aided navigation system and an artificial po-tential field (APF) guidance algorithm which work together to command a swarm of chasers to safely rendezvous with the RSO. We present the MARVIN architec-ture and hardware-in-the-loop experiments demonstrating autonomous, collabo-rative swarm satellite operations successfully guiding three drones to rendezvous with a physical mockup of a non-cooperative satellite in motion.

The robustness and accuracy of a vision system for motion estimation of a tumbling target satellite are enhanced by an adaptive Kalman filter. This allows a vision-guided robot to complete the grasping of the target even if occlusion occurs during the operation. A complete dynamics model, including aspects of orbital mechanics, is incorporated for accurate estimation. Based on the model, an adaptive Kalman filter is developed that estimates not only the system states but also all the model parameters such as the inertia ratio, center-of-mass, and the rotation of the principal axes of the target satellite. An experiment is conducted by using a robotic arm to move a satellite mockup according to orbital mechanics while the satellite pose is measured by a laser camera system. The measurements are sent to the Kalman filter, which, in turn, drives another robotic arm to grasp the target. The results demonstrate successful grasping even if the vision system is blocked for several seconds.

This paper presents a fault-tolerant 3D vision system for autonomous robotic operation. In particular, pose estimation of space objects is achieved using 3D vision data in an integrated Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by the state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states but also its inertial parameters. This allows the motion of the target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to the increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking. In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in a closed-loop configuration.

In this paper are described the image processing algorithms developed by SENER, Ingenieria y Sistemas to cope with the problem of image-based, autonomous rendez-vous (RV) with an orbiting satellite. The methods developed have a direct application in the OLEV (Orbital Life Extension Extension Vehicle) mission. OLEV is a commercial mission under development by a consortium formed by Swedish Space Corporation, Kayser-Threde and SENER, aimed to extend the operational life of geostationary telecommunication satellites by supplying them control, navigation and guidance services. OLEV is planned to use a set of cameras to determine the angular position and distance to the client satellite during the complete phases of rendez-vous and docking, thus enabling the operation with satellites not equipped with any specific navigational aid to provide support during the approach. The ability to operate with un-equipped client satellites significantly expands the range of applicability of the system under development, compared to other competing video technologies already tested in previous spatial missions, such as the ones described here below.