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This paper presents an experimental activity aimed at assessing performance of techniques for inertia and attitude parameters estimation of an uncooperative but known space target. The adopted experimental set-up includes a scaled-down 3D printed satellite mock-up, a spherical air bearing and a low-cost solid-state LIDAR. The experimental facility also comprises a motion capture system to obtain a benchmark of the pose (position and attitude) parameters and an ad-hoc designed passive balancing system to keep the centre of mass as close as possible to the centre of rotation. The LIDAR-based 3D point clouds, collected while the target rotates on the spherical air-bearing to reproduce the rotational dynamics of an almost-free rigid body, are processed to obtain pose and angular velocity estimate. Those data are used to determine the inertia properties of the target by solving a linear system based on the integral form of Euler equations. Performance and robustness of the algorithm for attitude and inertia properties estimation are assessed considering both the inertia benchmark provided by a high-fidelity CAD model of the target and the pose solution obtained from the motion tracking system and its extrinsic calibration with respect to the LIDAR.

This paper presents an innovative multi-sensor relative navigation module conceived to enable close-proximity operations for on-orbit servicing of small satellites. The module is composed of a Near-Infrared Laser Range Finder, acting both as range measurer and illuminator, and a monocular camera able to detect a set of reflecting fiducial markers installed on the target for pose estimation. Sensor fusion is performed in several points of the proposed processing pipeline, first following a cross-cueing logic in which the range aids the pose determination process, and later through the integration of both measurements into a Multiplicative Extended Kalman Filter for relative state estimation. The entire relative navigation architecture is first tested by numerically reproducing a close-range rendezvous trajectory of a chaser spacecraft moving toward the target, and using the open-source software Blender for synthetic image generation. Simulations are aimed at determining nominal relative state estimation accuracy (also considering variable sensors' acquisition rates) as well as performance under non-nominal conditions (e.g., due to a temporary loss of measurements). Focusing on the image processing and pose estimation pipeline, an in-depth analysis of the computational efficiency is then carried out by running the proposed algorithms on an embedded processing unit (i.e., the Raspberry Pi 4 Model B) using both synthetic data and real images collected within a dedicated experimental testbed. Numerical and experimental results demonstrate capability to attain pose and relative state estimates in line with accuracy requirements to ensure autonomous and safe close-proximity operations, e.g., up to sub-cm and sub-degree error levels in relative position and attitude respectively. In addition, the algorithm's computational runtime has shown to be compatible with real-time implementation at an acquisition frequency of 2 Hz.

Future Active Debris Removal missions will require an autonomous spacecraft (chaser) to safely monitor at close distance, and then approach and dispose an inactive artificial space object (target). Since these targets are uncooperative, meaning that they cannot provide any hint or help to the chaser navigation system, such operations require the target-chaser relative state and the target inertia parameters to be accurately estimated relying only on measurements from active or passive Electro-Optical sensors. In this framework, this paper proposes an original multi-step architecture for the estimation of the relative motion and inertia parameters of an uncooperative target during a close-range monitoring trajectory. In the first phase, LIDAR-based pose measurements and a smoothing approach are used to retrieve accurate, linearly independent estimates of the target angular velocity. These estimates are then used to compute the target's moments of inertia ratios solving a linear system based on the conservation equation for the angular momentum. Once the inertia parameters are accurately estimated, the LIDAR-based pose measurements are used to feed an Unscented Kalman Filter to determine the full relative state according to a loosely coupled configuration. The architecture foresees autonomous failure detection strategies to avoid divergence in the relative state estimation error caused by unavoidable, unfavorable target observation conditions occurring during the monitoring trajectory. Performance assessment is carried out through numerical simulations realistically reproducing close-range relative motion dynamics and LIDAR sensor operation, and considering targets characterized by highly variable size, shape, and orbital dynamics as test cases.

This paper describes the design, characterization and preliminary testing of a relative navigation module based on electro-optical sensors and tailored for close-proximity operations with respect to passively cooperative targets of the CubeSat class. The proposed unit relies on a laser range finder with a relatively large beam divergence and a visual camera. The laser range finder operates in the near-infrared band and is exploited to both provide direct range measurements and actively illuminate the scene. The visual sensor and its optics are coherently chosen and allow obtaining full pose (i.e., relative position and attitude) measurements by processing the acquired target’s images. The pose estimation procedure is based on the detection and identification of a set of fiducial markers installed on the target surface and highly reflective in the near-infrared band. Experimental tests are carried out at components level to both characterise the laser range finder and preliminarily assess the performance of the proposed pose determination approach. The results show that the proposed approach is robust to a large number of outliers, produced by highly reflective insulation layers typically covering satellite surfaces. Also, in all the considered test cases pose estimates with pixel-level reprojection errors, corresponding to sub-millimetre accuracy, are obtained.

This paper tackles the problem of spacecraft relative navigation to support the reach and capture of a passively cooperative space target using a chaser platform equipped with a robotic arm in the frame of future operations such as On Orbit Servicing and Active Debris Removal. Specifically, it presents a pose determination architecture based on monocular cameras to deal with a space target in GEO equipped with retro-reflective and black-and-white fiducial markers. The proposed architecture covers the entire processing pipeline, i.e., starting from markers’ detection and identification up to pose estimation by solving the Perspective-n-Points problem with a customized implementation of the Levenberg–Marquardt algorithm. It is designed to obtain relative position and attitude measurements of the target’s main body with respect to the chaser, as well as of the robotic arm’s end effector with respect to the selected grasping point. The design of the configuration of fiducial markers to be installed on the target’s approach face to support the pose determination task is also described. A performance assessment is carried out by means of numerical simulations using the Planet and Asteroid Natural Scene Generation Utility tool to produce realistic synthetic images of the target. The proposed approach robustness is evaluated against variable illumination scenarios and considering different uncertainty levels in the knowledge of initial conditions and camera intrinsic parameters.

This paper presents a strategy to adaptively select the field of view and the resolution of a scanning LIDAR in the frame of close-proximity operations with non-cooperative space targets. Specifically, the idea is to relate the selection of these operational parameters to the estimated target-chaser relative distance as well as to the size occupied by the target in the sensor field of view, with the goal to improve the performance of the relative navigation system. The proposed method is supported by a chaser attitude control strategy which allows keeping the sensor boresight axis always pointed towards the center of mass of the target. The performance achieved by the adoption of this strategy is evaluated by simulating two types of close-range trajectories in a numerical simulation environment which reproduces both the operation of a scanning LIDAR and the relative motion between two spacecraft.

Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters.

The potentialities of In-Orbit Servicing (IOS) to extend the operational life of satellites and the need to implement Active Debris Removal (ADR) to effectively tackle the space debris problem are well known among the space community. Research on technical solutions to enable this class of missions is thriving, also pushed by the development of new generation sensors and control systems. Among private companies, space agencies and universities, the European Space Agency (ESA) has been developing technologies in this field for decades. Several solutions have been proposed over the years to safely capture orbital objects, the majority relying on robotic systems. A promising option is the employment of an autonomous spacecraft (chaser) equipped with a highly dexterous robotic arm able to perform the berthing with a resident space object. This operation poses complex technical challenges both during the approach phase and after contact. In this respect, the design of an effective, reliable, and robust Guidance, Navigation and Control (GNC) system, for which several algorithmic architectures and hardware configurations are possible, plays a key role to ensure safe mission execution. This work presents the outcomes of a research activity performed by a consortium of universities under contract with ESA with the goal to develop the navigation and control subsystems of a GNC system for controlling a chaser equipped with a redundant manipulator. Both the final approach until capture and the target stabilization phase after capture are considered in the study. The proposed solution aims at the implementation of a combined control strategy. Robust control methods are adopted to design control laws for the uncertain, nonlinear dynamics of the chaser and of the complete chaser–target stack after capture. Visual–based solutions, i.e., relying on active/passive electro– optical sensors, are selected for relative navigation. A complete sensor suite for relative and absolute navigation is part of the GNC system, including transducers for robot joint measurements. To properly validate the proposed solutions, a complete numerical simulator has been developed. This software tool allows to thoroughly assess the system performance, accounting for all the relevant external disturbances and error sources. A realistic synthetic image generator is also used for relative navigation performance assessment. This paper presents the design solutions and the results of preliminary numerical testing, considering three mission scenarios to prove the flexibility of the solution and its applicability to a wide range of operational cases.

The paper focuses on guidance and relative navigation aspects in the framework of an Active Debris Removal mission. Specifically, an original multi-step strategy, inspired by the one designed for the OSAM-1 mission, is proposed to perform rendezvous toward an uncooperative target reaching a relative distance suitable for monitoring using Electro-Optical sensors. At each step, passive safety is guaranteed by setting pre-defined regions of interest that cannot be breached by the chaser until the mission control on ground provides an Authority-to-Proceed command. As regards the navigation aspects, particular attention is paid to the problem of obtaining highly accurate relative state estimates in close-proximity conditions using a LIDAR. Finally, an ad-hoc reconfiguration strategy is proposed to keep the chaser within a limited along-track distance from the target. The reconfiguration manoeuvre is commanded on the basis of the LIDAR-based relative state estimates. Performance assessment is carried out by means of numerical simulations.

The concepts of On-Orbit Servicing and Active Debris Removal have been introduced to increase the operating life of active satellites and as a debris remediation measure, respectively. For these operations, a servicing spacecraft (chaser) must orbit safely and autonomously in close proximity to a target that is typically known, but non-cooperative. So, advanced guidance, navigation and control functions are required to carry out these missions. This work focuses on the problem of estimating the target-chaser relative state and the target inertia properties exploiting measurements from a LIDAR during the different phases of a close-range rendezvous maneuver. Specifically, the relative trajectory is designed to provide both safety and optimal observation conditions. To this aim, it is composed of multiple safety ellipses for monitoring purposes, in which the target-chaser relative distance is of the order of tens of meters, followed by a R-bar trajectory for the final approach. In this scenario, the LIDAR-based relative navigation solution is a multi-step processing architecture, which exploits pose determination techniques, unscented Kalman filtering and a linear least squares solver. The performance of this approach is evaluated by means of numerical simulations.

Being able to perform autonomous relative navigation with respect to an uncooperative and possibly unknown space target is a critical task in the framework of on-orbit servicing and active debris removal applications. Electro-Optical sensors are required because of the non-cooperative nature of the target. Specifically, the solution proposed in this work is a LIDAR-based relative navigation architecture, which combines, in a loosely-coupled configuration, a pose determination algorithm, designed to process raw LIDAR data (i.e. point clouds), with an unscented Kalman filter to estimate relative motion parameters as well as inertia properties of the target. The developed algorithm is tested in a numerical simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft.