Active Debris Removal Missions Research Articles

Optimal attitude planning along torque equilibrium points using aerodynamic database for deorbiting large space debris

Currently, considerable attention is being directed toward active debris removal (ADR) using small, cost-effective satellites. From high altitude to re-entry, ADR missions use thrusters that consume significant amounts of fuel. The volume of the propulsion system in these satellites can be quite large, and it is difficult for a small satellite to accommodate a large-scale propulsion system. Thus, active utilization of atmospheric drag is being considered for small satellites with low capability. However, the principal challenge for small satellites to remove large debris is to deal with the large disturbance torque caused by strong atmospheric drag on the debris. This paper proposes attitude guidance around an equilibrium (balance) point in attitude dynamics acted on by gravity gradient and aerodynamic torques. The desired attitude is set to optimize two conflicting objectives: maximizing the projection area to generate large aerodynamic force and minimizing the disturbance torque. In this paper, we develop an aerodynamic database (ADDB) by rigorously calculating the aerodynamic torque for each altitude and solar array paddle (SAP) rotation based on an aerodynamic characteristic model of free molecular flow. Using the ADDB, the SAP angle and orbit altitude effects on the torque equilibrium attitude (TEA) are clarified. Then, the mean disturbance torque map at each orbit altitude is introduced to obtain the candidate TEA paths. The effectiveness of the proposed paths for debris descent is demonstrated through numerical simulations.

Two-stage estimator for the complete inertia tensor of uncooperative debris on CubeSat based Active Debris Removal missions

The threat of debris collisions in Low Earth Orbit (LEO) has driven many researchers to develop Active Debris Removal (ADR) missions to manage large debris in LEO. A modern method of capturing debris with a non-rigid tether has been a recent research focus because of difficult post capture dynamics associated with controlling an uncooperative debris through a tether. To facilitate these control schemes, this paper proposes a new two-stage inertia estimator that estimates all principal and products of inertia for an uncooperative space debris using LiDAR and tether force measurements made on-board a CubeSat sized chaser satellite. The method proposed in this work estimates the debris center of mass with a novel pseudo measurement Kalman Filter so that the full inertia tensor can be subsequently estimated using an additional iterative parameter identification algorithm. In addition, the paper achieves online inertia estimation without assuming the tether connection point on the debris by approximating its location using tether tension measurements. The proposed control and estimation scheme is shown in this paper to estimate the full debris inertia tensor even with frequent tether slackness. Two simulation scenarios are presented in this paper, one where the connection point of the tether on the debris is approximated using noiseless measurements of tether tension, and a second where the location of the tether connection point is approximated using noisy measurements of tether tension. For the simulation conditions used in this research, it is shown that the proposed two-stage estimator (TSE) is successful for both cases that the debris is inertially asymmetrical and tri-inertial with non-zero products of inertia.

Parameter Identification of Space Manipulator's Flexible Joint

It is considered to use a manipulator mounted on a satellite in order to perform active debris removal missions. Space manipulator control system needs to take the dynamic model of the satellite-manipulator system into account because of the influence of the manipulator motion on the position and attitude of the satellite. Therefore, precise modelling of the space manipulator dynamics as well as parameter identification are needed in order to improve the credibility of the simulation tools. In this paper we presented the identification of the flexible-joint space manipulator model based on dynamic equations of motion. Experiments were performed in emulated microgravity environment using planar air bearings. The arbitrarily selected joint-space trajectory was performed by the manipulator’s control system. The experiments were repeated multiple times in order to analyze the identification method sensitivity. The identification is based on Simulink SimMechanics model. Thus, the procedure can be used for any space manipulator without the need of obtaining analytical relations for dynamic equations each time. Including joint flexibility and spring viscous damping in the dynamic model allowed to reflect the experimental measurements better than the reference model could. Identified parameters of the flexible joint have values of the same magnitude as corresponding real system parameters.

Active 6 DoF Force/Torque Control Based on Dynamic Jacobian for Free-Floating Space Manipulator

Abstract In-orbit capture of a non-cooperative satellite will be a major challenge in the proposed servicing and active debris removal missions. The contact forces between the manipulator end-effector and the elements of the target object will occur in the grasping phase. In this paper, an active 6 Degrees of Freedom (DoF) force/torque control method for manipulator mounted on a free-floating servicing satellite is proposed. The main aim of the presented method is to balance the relation between end-effector position and force along each direction in the Cartesian space. The control law is based on the Dynamic Jacobian, which takes into account the influence of the manipulator motion on the state of the servicing satellite. The proposed approach is validated in numerical simulations with a simplified model of contact. Comparison with the classical Cartesian control shows that the active 6 DoF force/torque control method allows to obtain better positioning accuracy of the end-effector and lower control torques in manipulator joints in the presence of external forces.

A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots.

Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.

Detumbling of Underactuated Tethered Satellite System Based on Hierarchical Sliding Mode Control

Millions of space debris pose a significant risk to operational spacecraft, making active debris removal (ADR) missions an urgent priority. Net capturing is a promising method to remove space debris objects due to its multiple advantages. However, once a target has been captured, a combined tethered system comprising the chaser, the target, and the tethers is formed, which requires effective stabilization. In this paper, stabilization of the entire postcapture system is realized by solely controlling the active chaser satellite. Such a control strategy can attenuate system hardware facilities compared with those by applying additional tether control such as using a winding mechanism, which were mostly discussed in the literature. Further, for the first time, considering the uncertainties during the capture process, including inaccurate target parameters, initial attitude error of the target, and external disturbances, a hierarchical sliding mode controller (HSMC) with two sublayers is developed to detumble this underactuated system. Additionally, a hybrid position controller is proposed to eliminate the undesired swing motion within the system. Numerical simulations demonstrate that HSMC method delivers better performance over the proportional-derivative controller in efficiency and robustness for postcapture stabilization of the combined tethered system. Overall, our proposed control strategy offers a promising solution for future ADR missions, contributing to a safer and more sustainable space environment.

Risk Index for the Optimal Ranking of Active Debris Removal Targets

The first step of any active debris removal (ADR) mission is the selection of the target. The optimal choice is to find the most dangerous debris that can be removed considering the chaser spacecraft requirements and mission constraints. After creating a catalog of the current space population in low Earth orbit (LEO), the MIT Monte Carlo Orbital Capacity Assessment Tool (MOCAT-MC) is used to simulate and predict the future space environment and the interaction among the space population. A novel performance index to quantify the criticality of each debris and the risk that it presents to the space environment is proposed. The new risk index considers the proximity of the debris to highly populated regions, its persistence in orbit, its likelihood to collide, and the estimated number and mass of debris that it can generate. The risk index is then optimized to work either with the full space population or a subset of it, where the ranking of risk among debris is highlighted. Multiple risk analyses are proposed in the test cases, where the ranked list of optimal targets is provided.

Characterization of deorbiting satellites and space debris with radar

During deorbiting, be it natural orbital decay or otherwise (e.g. self-disposal or active debris removal), precise knowledge of the condition of the target is of critical importance. The Tracking and Imaging Radar (TIRA) is an instrument that possesses the ability to accurately determine satellite and space debris orbits while also generating detailed radar images of them.We present recent results from the characterization of deorbiting satellites and space debris employing TIRA. Drawing examples from orbital decay events (Tiangong-1, Soyuz 2.1v R/B), deorbiting (Tiangong-2) and self-disposal operations (Carbonite-1, MicroSCOPE), we demonstrate the potential of TIRA. Furthermore, we focus on the emerging field of active debris removal (ADR), where reliable information of the on-orbit status and attitude motion of potential targets can “make or break” an ADR mission. In this context, the on-orbit status of a JAXA R/B (43682) and a VESPA adapter (39162) are investigated.We employ inverse synthetic aperture radar (ISAR) imaging and 3D modelling of ISAR images, obtained with TIRA, to characterize the status and attitude motion of satellites and space debris. When certain conditions render direct imaging unsuitable, the more powerful L-band tracking radar of TIRA is used to obtain and analyze the temporal evolution of their radar cross-section (RCS). In this work, we employ for the first time, a wavelet time–frequency analysis of such RCS signatures, specifically of NABEO-1 (43692) in LEO and an Ariane 5 rocket body (28904) in geostationary transfer orbit (GTO), to characterize their on-orbit status. The wavelet analysis delivers results in good agreement with more traditional tools, with added information on time localization of periodicities. We are thus able to confirm the tumbling motion of NABEO-1 and constrain the rotational velocity of Ariane 5 upper stage (28904).

Active debris removal: A review and case study on LEOPARD Phase 0-A mission

The growing number of space debris is alarming as it threatens space-borne services. Hence, there is an increasing demand to remove space debris to ensure sustainability and protect valuable orbital assets. Over the past few years, the research community, agencies and industries have studied many passive and active debris removal methods. However, the current technology readiness for space debris removal is still low. This paper first presents a comparative study of various space debris removal technologies to address the knowledge gap and quantify the challenges. This paper reviews the current state-of-the-art space technologies relevant to Active Debris Removal (ADR) missions. Detailed trade-off analysis is then presented based on the Low Earth Orbit Pursuit for Active Removal of Debris (LEOPARD) Phase 0-A study; this study is part of the United Kingdom (UK) Space Agency’s Active Debris Removal programme. The ADR mission scenario considered in this paper comprises a chaser spacecraft equipped with recommended technologies to capture non-cooperative targets safely. The final capture technology for the LEOPARD mission consists of an active robotic manipulator and a passive net capture mechanism. An analysis of the coupled-body dynamics of the chaser spacecraft carrying the robot manipulator and the targeted debris is carried out in simulation using SimscapeTM. The chaser spacecraft comprises Airbus’s Versatile In-Space and Planetary Arm (VISPA) mounted on a base spacecraft from Surrey Satellite Technology Ltd. (SSTL); the targeted debris is SSTL’s Tactical Operational Satellite (TOPSAT). The simulation results show dynamic changes in the chaser robot and the target satellite while performing non-cooperative capture. The simulation study accounted for various operational scenarios where the target is stationary or in motion. Further, for different modes of operation, the worst-case end-effector capture force limits were determined using open-loop control to execute a safe capture. Overall, the results presented in the paper advance the current state-of-the-art of robotic ADR and offer a significant leap in designing close-range motion and force control for stabilising the coupled multi-body system during capture and post-capture phases. In summary, this paper pinpoints the technological gaps, identifies barriers to realising ADR missions and offers solutions to catalyse technology maturity for protecting the space ecosystem.

Contact dynamic analysis of tether-net system for space debris capture using incremental potential formulation

An efficient computational method is proposed to combine the discrete differential geometry (DDG) method with the multi-dimensional incremental potential (IP) formulation to simulate the contact dynamics between a shooting net and a defunct satellite. The geometrically nonlinearity of the elastic net is described using the DDG method, which discretizes the flexible net into multiple nodes and connected edges. The internal elastic forces and the associated Jacobians are derived analytically. The multi-dimensional incremental potential formulation is used to achieve the intersection-free condition during the physical collision between deformable tether-net and rigid space debris. The smooth IP barrier functional constructed for contact simulation is differentiable, such that the nonlinear elastodynamic system can be robustly solved through a fully implicit time-marching scheme. Our numerical approach is first applied to simple examples, and demonstrating its effectiveness in dealing with non-penetration contacts of elements with different dimensions, such as ‘edge-to-edge’ and ‘edge-to-surface’. Next, the dynamic interaction between the deploying net and defunct satellite is analysed. A parameter sweep is performed with our robust and efficient simulator to show the configuration evolution of the net shot from different relative locations of the target object. Our numerical results can provide an optimal deployment strategy for tether-net capture systems during the active debris removal missions.

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.

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.

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.

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.

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.

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.

Estimation of uncooperative space debris inertial parameters after tether capture

Space debris is often uncooperative and unresponsive, necessitating the understanding of its motion for successful post-capture control in an active debris removal mission. To this end, the inertia tensor of the captured debris object must be estimated, which is particularly challenging when the connection between chaser and target is not rigid, such as when achieved through a tether. In this work, the principal moments of inertia of tethered debris are estimated by means of Extended Kalman Filters and an Unscented Kalman Filter, using measurements of the rotational rates of the target. Two cases are analyzed in which (i) the chaser–tether–target system is subject to frequent tether slackness and (ii) the tether is almost continuously taut. The performance of the filters are analyzed via Monte-Carlo simulations. It is demonstrated that two of the principal moments of inertia can be estimated well, assuming knowledge of the tension in the tether and of the tether attachment point on the debris. It is found that, although the Extended Kalman Filter is capable of estimating the moments of inertia, it is inadequate due to the large variance of its final moment of inertia estimates. The Unscented Kalman Filter, instead, is capable of obtaining precise and accurate estimates of the moments of inertia after tethered capture of debris.

Application of bidirectional rapidly exploring random trees (BiRRT) algorithm for collision-free trajectory planning of free-floating space manipulator

AbstractOn-orbit servicing and active debris removal missions will rely on the use of unmanned satellite equipped with a manipulator. Capture of the target object will be the most challenging phase of these missions. During the capture manoeuvre, the manipulator must avoid collisions with elements of the target object (e.g., solar panels). The dynamic equations of the satellite-manipulator system must be used during the trajectory planning because the motion of the manipulator influences the position and orientation of the satellite. In this paper, we propose application of the bidirectional rapidly exploring random trees (BiRRT) algorithm for planning a collision-free trajectory of a manipulator mounted on a free-floating satellite. A new approach based on pseudo-velocities method (PVM) is used for construction of nodes of the trajectory tree. Initial nodes of the second tree are selected from the set of potential final configurations of the system. The proposed method is validated in numerical simulations performed for a planar case (3-DoF manipulator). The obtained results are compared with the results obtained with two other trajectory planning methods based on the RRT algorithm. It is shown that in a simple test scenario, the proposed BiRRT PVM algorithm results in a lower manipulator tip position error. In a more difficult test scenario, only the proposed method was able to find a solution. Practical applicability of the BiRRT PVM method is demonstrated in experiments performed on a planar air-bearing microgravity simulator where the trajectory is realised by a manipulator mounted on a mock-up of the free-floating servicing satellite.