Active Debris Removal Research Articles

Relative Navigation Strategy About Unknown and Uncooperative Targets

In recent years, space debris has become a threat for satellites operating in low Earth orbit. Even by applying mitigation guidelines, their number will still increase over the course of the century. As a consequence, active debris removal missions and on-orbit servicing missions have gained momentum at both academic and industrial level. The crucial step in both scenarios is the capability of navigating in the neighborhood of a target resident space object. This problem has been tackled many times in literature with varying level of cooperativeness of the target required. While several techniques are available when the target is cooperative or its shape is known, no approach is mature enough to deal with uncooperative and unknown targets. This paper proposes a hybrid method to tackle this issue called Coarse Model-Based Relative Navigation (CoMBiNa). The main idea of this algorithm is to split the mission into two phases. During the first phase, the algorithm constructs a coarse model of the target. In the second phase, this coarse model is used as a reference for a relative navigation technique, effectively shifting the focus toward state and inertia estimation. In addition, this paper proposes a strategy to leverage the structure of the selected navigation method to detect and reject outliers. To conclude, CoMBiNa is tested on a simulated environment to highlight its benefits and its shortcomings, while also assessing its applicability on a limited-resource single-board computer.

The economics of satellite deorbiting performance bonds

The rapid accumulation of orbital debris, coupled with the exponentially increasing number of satellites in certain regions of orbital space, impedes and may preclude long-term utilization of these resources. While voluntary satellite deorbiting guidelines are demonstrably ineffective in incentivizing firms to deorbit their expended satellites, we suggest interest-bearing deorbiting performance bonds provide an incentive mechanism that has minimal effect on firms’ operating costs. Not only would this type of bond reduce the rate of growth in orbital debris, these bonds could be tradeable, thus establishing market prices for certain types of orbital debris, both stimulating the market for active debris removal and further encouraging investments in satellite robustness.

A Flytrap‐Inspired Bistable Origami‐Based Gripper for Rapid Active Debris Removal

Space debris is considered an increasingly serious threat to on‐orbit spacecrafts. There are several potential solutions to this problem, including active debris removal. Flexible robots have shown promising adaptability and dexterity in soft manipulation owing to their inherent compliance. This compliance allows them to interact safely and efficiently during space missions such as active debris removal. Herein, inspired by the bistable structure and energy‐release mechanism of the Venus flytrap, a bistable origami‐based gripper is developed. The flexible gripper, which can rapidly achieve stable state switching, is in the form of a biomimetic flytrap leaf curvature and is actuated using a shape memory alloy actuator. Subsequently, a flytrap bristle‐like locking structure is used to ensure locking via the action of a dielectric elastomer actuator to alleviate the vibration instability of the flexible robot under rapid actuation. The experimental results showed that the flexible gripper can achieve effective capture within approximately 300 ms. In addition, it exhibits good adaptability and mechanical robustness with targets having complex shapes and sizes, indicating its potential applications in the space capture and sampling fields.

Possible approach to establish international rules of emerging space activities - risk-based approach and adaptive governance

In May 2021, the Supplementary Requirements for a License to Operate a Spacecraft Performing On-Orbit Servicing were established and published in Japan. The Supplementary Requirements were made as the Rules for a person who intends to carry out on-orbit servicing mission including active debris removal. The Rules were to be reflected in the licensing schemes within the already existent framework of the Space Activities Act, which provides the requirements to obtain a license to control a spacecraft in Japan. Consisting of legal, technical, and organizational requirements, the Rules provide a framework to carry out on-orbit servicing mission based on the agreements and consents among the stakeholders. The Rules address the legal and technical risks derived especially from the rendezvous and proximity operations phases in the on-orbit servicing mission profile. This framework implicates the principles of safe and transparent operations as the norms for the on-orbit servicing missions, and its approach demonstrates adaptive governance in the sense that the Rules were deliberated among multi-sector stakeholders, they are expected to be introduced and extended internationally, and they are flexible to technology development. This paper explains the Rules from the points of view of risk-based approach and adaptive governance and aims to gain the perspectives on the possible approaches to establish the international rules and norms for emerging space activities.

Active Debris Removal Mission Planning Method Based on Machine Learning

To prevent the proliferation of space debris and stabilize the space environment, active debris removal (ADR) has increasingly gained public concern. Considering the complexity of space operations and the viability of ADR missions, it would be necessary to schedule the ADR process in order to remove as much debris as possible. This paper presents an active debris removal mission planning problem, devoted to generate an optimal debris removal plan to guide the mission process. According to the problem characteristics, a two-layer time-dependent traveling salesman problem(TSP) mathematical model is established, involving the debris removal sequence planning and the transfer trajectory planning. Subsequently, two main novel methods based on machine learning are proposed for the ADR mission planning problem, including a deep neural networks(DNN)-based estimation method for approximating the optimal velocity increments of perturbed multiple-impulse rendezvous and an reinforcement learning(RL)-based method for optimizing the sequence of debris removal and rendezvous time. Experimental results of different simulation scenarios have verified the effectiveness and superiority of the proposed method, indicating the good performance for solving the active debris removal mission planning problem.

Barriers and Gateways to Cleaning Up Earth Orbit: The Legal, Economic, and Political Dimensions of Debris Remediation

The preponderance of human-made debris in Earth orbit has long been recognized as a significant threat to continued safe operations in space. National space agencies and private space companies are paying considerable attention to the technical questions surrounding the active removal of debris (ADR). Nonetheless there are still significant legal, economic, and geopolitical questions that need to be addressed before any meaningful and sustained industry around the removal of debris can be established. This discussion will focus on identifying the legal issues of responsibility, jurisdiction and liability and proposing possible solutions within the current nexus of international agreements. Yet these must be considered alongside the economic roadblocks to funding ADR; the challenges for states looking to collaborate in current geopolitical circumstances; and the often overlooked ‘data deficit’ in respect of the space surveillance and tracking (SST) capabilities needed to enable the safe removal of debris. It is contended that any system for managing ADR, Debris Mitigation and general protection of the space environment should not emerge in isolation. A coherent system of managing and coordinating the orbital environment needs to be developed. This article will argue that it is for individual nations to show political leadership and an economic commitment to properly manage the delicate extraterrestrial environment. Failure to do so will represent a missed opportunity and leave a troubling legacy for future generations. Active Debris Removal, Astropolitics, Space Law, Dual use technology, Space Environment

Learning Reference Governor for Constrained Spacecraft Rendezvous and Proximity Maneuvering

Spacecraft automated rendezvous, proximity maneuvering, and docking (ARPOD) play significant roles in many space missions, including on-orbit servicing and active debris removal. Precise modeling and prediction of spacecraft dynamics can be challenging due to the uncertainties and perturbation forces in the spacecraft operating environment and due to the multilayered structure of its nominal control system. Despite this complication, spacecraft maneuvers need to satisfy required constraints (thrust limits, line-of-sight cone constraints, relative velocity of approach constraints, etc.) to ensure safety and achieve ARPOD objectives. This paper considers an application of a learning-based reference governor (LRG) to spacecraft ARPOD operations to enforce constraints without relying on a dynamic model of the spacecraft during the mission. Similar to the conventional reference governor (RG), the LRG is an add-on supervisor to a closed-loop control system, serving as a prefilter on the command generated by the ARPOD planner. The LRG modifies, if it becomes necessary, the reference command to a constraint-admissible value to enforce specified constraints. The LRG is distinguished, however, by the ability to rely on learning instead of an explicit model of the system; and it guarantees constraints’ satisfaction during and after the learning. In this paper, the LRG is applied to the control of combined translational and rotational motions of a chaser spacecraft, and three case studies with different sets of safety constraints and thruster assumptions are used to demonstrate the benefits of the LRG in ARPOD missions.

Design and guidance of a multi-active debris removal mission

Space debris have become exceedingly dangerous over the years as the number of objects in orbit continues to increase. Active debris removal (ADR) missions have gained significant interest as effective means of mitigating the risk of collision between objects in space. This study focuses on developing a multi-ADR mission that utilizes controlled reentry and deorbiting. The mission comprises two spacecraft: a Servicer that brings debris to a low altitude and a Shepherd that rendezvous with the debris to later perform a controlled reentry. A preliminary mission design tool (PMDT) was developed to obtain time and fuel optimal trajectories for the proposed mission while considering the effect of J2, drag, eclipses, and duty cycle. The PMDT can perform such trajectory optimizations for multi-debris missions with computational time under a minute. Three guidance schemes are also studied, taking the PMDT solution as a reference to validate the design methodology and provide guidance solutions to this complex mission profile.

Relative visual navigation around an unknown and uncooperative spacecraft

A Robotic Active Debris Removal (ADR) mission requires close-proximity operations for capturing potentially tumbling, uncooperative targets and disposing of them. These operations rely on accurate relative navigation between the chaser and the target to safely perform the mission. In the case of a known piece of debris that has not been designed to be serviced or disposed of, the knowledge of its shape factor allows relative pose determination. In the case of unknown space debris, however, no relative pose determination is possible as no reference is available beforehand. In this paper, we derive an onboard software architecture for estimating the shape and dynamical model of an unknown, uncooperative space debris, and perform tracking, using features detected in the image and the associated depth. Firstly, an Error-State Kalman filter (ESKF) is used to estimate the chaser trajectory and attitude as well as the target shape in an arbitrarily fixed target frame. The next step in the architecture is to apply a Gaussian Processes (GPs) algorithm to model the rotational dynamics of the debris, allowing an accurate attitude propagation independently of its tumbling mode. Finally, a second ESKF is instantiated using existing knowledge to track the position and attitude of the target. This work assumes the pre-existence of a feature detection, tracking and matching algorithm, and fusing data from a depth sensor allowing the retrieval of pixel coordinates and depth for each detected feature. The proposed architecture has been tested and validated in a computer-simulated environment with parameters representative of a real-world ADR mission. Results from over 100 simulations show a mean position and attitude error of 0.3 m and 1.5°, respectively.

A multi-platform active debris removal mission planning method based on DCOP with chain topology

In this work, a distributed multi-platform active debris removal mission planning task is formulated as a distributed constrained optimization problem (DCOP), and a novel Synchronous Incomplete Searching Algorithm under Chain Topology (SISACT) is proposed. We envision a distributive cooperative mission planning scenario where multiple debris removal platforms generate individual mission sub-plans on how to remove a subset of assigned debris subject to constraints on mission duration and orbit transfer cost. The overall goal is to maximize the aggregate reward for debris removal while avoiding conflicts among sub-plans. To resolve the potential conflicts, multiple platforms must communicate each other intensively. The proposed SISACT algorithm can reduce the inter-platform communication burdens by inducing a fully connected constraint graph into a chain graph with domain assignment. SISACT is designed to focus on global collaboration among the platforms for mission plan partitioning while leveraging existing single platform mission planning algorithms to provide details of the sub-plans. Simulation with a realistic active debris removal scenario has been carried out. It is shown that the proposed SISACT algorithm can optimize the overall reward of the mission while satisfying all the constraints. This validates the effectiveness of SISACT. • A new multi-platform ADR mission planning is proposed based on DCOP. • A new DCOP model with fully-connected all-hard constraint graph is presented. • Proposed method can satisfy all constraints without changing solution frequently. • Tests on Iridium33 debris set with 4 platforms verify its efficiency.

An estimate of expected economic losses from satellite collisions with orbital debris

We estimate the expected annual cost of satellite collisions with orbital debris and find that the expected annual loss for all orbits was $86-103 million in 2020. Nearly all expected losses, $79-102 million, were in LEO, and these losses represent approximately 0.16% of the value of operational satellites in that orbit. Over 70% of losses occurred in the 600-900 km orbital band. Commercial satellites’ share of expected losses is approximately one-third of expected total losses. These findings may have implications for policy, in particular relating to the market for active debris removal.

Rendezvous and proximity operations design of an active debris removal service to a large constellation fleet

This work presents the design of rendezvous and close proximity operations for an active debris removal service for maintenance and disposal of a large constellation fleet. The design drivers of the service are safety and robustness of the operations, capability to remove a satellite under every condition, and high level of autonomy to reduce the service-related operations’ cost. Accordingly, the most challenging case of a rendezvous with a uncooperative non-collaborative target satellite is considered and a strategy to reduce its rotational motion is also presented. As for the rendezvous, the focus is on the relative trajectory design. First a guidance solution capable to maximise the observability property of the angles-only relative navigation problem is developed. Second, a set of convenient relative trajectories to enable target inspection for various illumination conditions is selected and an approach to ensure safe transitions among them is settled. Both these designed strategies can be readily implemented on autonomous spaceborne systems. Simulations are run for two constellation fleets, differing in mass of the single s/c and orbit’s altitude and inclination.

Simplified optimization model for low-thrust perturbed rendezvous between low-eccentricity orbits

Trajectory optimization of low-thrust perturbed orbit rendezvous is a crucial technology for space missions in low Earth orbits, which is difficult to solve due to its initial value sensitivity, especially when the transfer trajectory has many revolutions. This paper investigated the time-fixed perturbed orbit rendezvous between low-eccentricity orbits and proposed a priori quasi-optimal thrust strategy to simplify the problem into a parametric optimization problem, which significantly reduces the complexity. The optimal trajectory is divided into three stages including transfer to a certain intermediate orbit, thrust-off drifting and transfer from intermediate orbit to the target orbit. In the two transfer stages, the spacecraft is assumed to use a parametric law of thrust. Then, the optimization model can be then obtained using very few unknowns. Finally, a differential evolution algorithm is adopted to solve the simplified optimization model and an analytical correction process is proposed to eliminate the numerical errors. Simulation results and comparisons with previous methods proved this new method’s efficiency and high precision for low-eccentricity orbits. The method can be well applied to premilitary analysis and high-precision trajectory optimization of missions such as in-orbit service and active debris removal in low Earth orbits.

Deep Learning Based Target Pose Estimation Using LiDAR Measurements in Active Debris Removal Operations

Deep Learning Based Target Pose Estimation Using LiDAR Measurements in Active Debris Removal Operations

Evolutionary Optimization for Active Debris Removal Mission Planning

Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region of space. Such a problem is known to be computationally demanding and the present paper provides a technique for preliminary mission planning based on a novel evolutionary optimization algorithm, which identifies the best sequence of debris to be captured and/or deorbited. An original archipelago structure is adopted for improving algorithm capabilities to explore the search space. Several crossover and mutation operators and migration schemes are also tested in order to identify the best set of algorithm parameters for the considered class of optimization problems. The algorithm is numerically tested for a fictitious cloud of debris in the neighbourhood of Sun-synchronous orbit, including cases with multiple chasers.

Localization and classification of space objects using EfficientDet detector for space situational awareness

Space situational awareness (SSA) systems play a significant role in space navigation missions. One of the most essential tasks of this system is to recognize space objects such as spacecrafts and debris for various purposes including active debris removal, on-orbit servicing, and satellite formation. The complexity of object recognition in space is due to several sensing conditions, including the variety of object sizes with high contrast, low signal-to-noise ratio, noisy backgrounds, and several orbital scenarios. Existing methods have targeted the classification of images containing space objects with complex backgrounds using various convolutional neural networks. These methods sometimes lose attention on the objects in these images, which leads to misclassification and low accuracy. This paper proposes a decision fusion method that involves training an EfficientDet model with an EfficientNet-v2 backbone to detect space objects. Furthermore, the detected objects were augmented by blurring and by adding noise, and were then passed into the EfficientNet-B4 model for training. The decisions from both models were fused to find the final category among 11 categories. The experiments were conducted by utilizing a recently developed space object dataset (SPARK) generated from realistic space simulation environments. The dataset consists of 11 categories of objects with 150,000 RGB images and 150,000 depth images. The proposed object detection solution yielded superior performance and its feasibility for use in real-world SSA systems was demonstrated. Results show significant improvement in accuracy (94%), and performance metric (1.9223%) for object classification and in mean precision (78.45%) and mean recall (92.00%) for object detection.

Trajectory optimization for multi-target Active Debris Removal missions

The proliferation of debris in Low Earth Orbit raises increasing challenges about the sustainability of the space environment. The current international recommendations to deorbit satellites at the end of their life are not sufficient to ensure this sustainability. To accelerate the process, it would be necessary to remove hazardous debris and thus de-congest the high-risk orbits. However, active debris removal missions are very expensive, and still in their early stages. One way to reduce its costs is to remove several pieces of debris per removal mission, which corresponds to a global optimization problem, with the objective of optimizing the number of debris removed while minimizing the cost in mission time and in propellant.In our study, the trajectory optimization is performed using a simulated annealing algorithm. The tool classifies the targets in groups gathered by similarities of orbital parameters, with the objective for each mission not to exceed a total ΔV=4 km/s for a mission time lower than 3 years. To achieve this, a weighting is made between out-of-plane maneuvers, costly in propellant, and the use of the orbital parameter Ω drift, costly in time, to allow the spacecraft to pass from one debris to another. A criterion is set up to determine from which threshold it is more interesting to carry out a maneuver on Ω rather than to let the gravitational drift act. As a result, applied on the 50 most hazardous debris established by the International Astronautical Federation, we obtain a classification of the 50 pieces of debris into 11 optimal groups, with an average ΔV of 3.05 km/s and an average mission time of 1.5 years, which corresponds to the imposed criteria and validates the feasibility of such ADR missions.

Simulation of Deployable Cable Nets for Active Debris Removal in Space

Deployable cable nets have been proposed as promising systems for the active removal of space debris. The modelling and analysis of such systems during deployment, capture, and post-capture phases are crucial for the effective design of an operative mission. To this aim, accurate and effective simulation tools are necessary. We propose a finite element model of the cable net with lumped nodal masses and first-order cable elements. The nodal positions are assumed as the main unknowns of the problem. The large displacements and finite deformations are described by the Green-Lagrange strain tensor. The cable elements are assumed to react only in tension. Global damping is considered in line with Rayleigh’s hypothesis. The governing equations are solved numerically by means of the Runge-Kutta method with a variable time step. As an illustrative example, we present the simulation of the in-plane deployment of a planar, square-mesh net. The proposed approach turns out to be computationally effective, even if the accuracy of the numerical integration scheme needs to be improved, particularly in the final stages of deployment.

MODELLING OF DEPLOYABLE CABLE NETS FOR ACTIVE SPACE DEBRIS REMOVAL

Space debris represent a true risk for current and future activities in the circumterrestrial space, and remediation activities must be set out to guarantee the access to space in the future. For active debris removal, the development of an effective capturing mechanism remains an open issue. Among several proposals, cable nets are light, easily packable, scalable, and versatile. Nonetheless, guidance, navigation, and control aspects are especially critical in both the capture and post-capture phases. We present a finite element model of a deployable cable net. We consider a lumped mass/cable net system taking into account non-linearities arising both from large displacements and deformations, and from the different response of cables when subject to tension and compression. The problem is stated by using the nodal coordinates as Lagrangian coordinates. Lastly, the nonlinear governing equations of the system are obtained in a form ready for numerical integration.

Momentum based classification for robotic active debris removal

Recent spacecraft collisions with debris and various near misses have highlighted the need for pursuing Active Debris Removal (ADR) of space debris. Robotic manipulators provide a versatile way to capture, detumble, and eventually deorbit the debris. This paper explores the classification of space debris and robotic manipulators based on angular momentum. Previous classifications have considered the tumble rates, size, and orbit of the debris. However, a momentum-based classification gives an additional insight into the method selection for debris removal as shown in this paper. A study on the momentum capture capabilities of previously flown robotic manipulators is performed. This gives an impression of the capabilities of the flight-heritage robotic manipulators for their use in ADR missions. Furthermore, an analysis is also performed on the momentum of cylindrical space debris as it closely represents the spent rocket upper stages which could be prime targets for a robotic ADR mission. The change in momentum due to the tumble rate and inertia is analysed for such cylindrical debris and they are then categorized based on their momentum. These analyses provide the data required to perform a matching of the momentum of the debris with the momentum capabilities of the existing robotic manipulators and thus classify the debris based on momentum. The classification is then applied to real debris objects and the results are discussed. The comparison of the momentum of debris and manipulators is also used to provide input for future manipulator development for ADR missions.