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As of 2024, there are 8000 metric tons of debris in orbit, comprising 75% of the Low Earth orbit environment.
Negative impact on astronomy
Risk of a collision cascade
Risk of damage to space stations and other active satellites.
An eerie display: Just before dawn, nearly half of the 150+ Global Meteor Network cameras capture parallel streaks of Starlink satellites gliding across the sky, causing a marked increase in false meteor detections. Here’s a glimpse of their signature trails on a co-added GMN image. (source: https://globalmeteornetwork.org)
Artist’s concept of satellites and space debris. The Kessler syndrome suggests that cascading collisions could create a ring of debris around Earth. Image via University of Miami.
Active Debris Removal (ADR)
Active Debris Removal (ADR)
ADR has been identified as a critical enabling technology for the next decade.
Several missions have been already proposed and implemented.
Many new methods of ADR are currently under research and testing.
ClearSpace-1: Earth´s First Space Debris Removal Mission (ClearSpace SA)
ADRAS-J mission (Astroscale)
Published work on ADR
Published work on ADR
Minduli Wijayatunga, Prof Roberto Armellin, Dr Harry Holt, Dr Laura Pirovano and Dr Aleksander Lidtke
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.
Minduli Wijayatunga, Prof Roberto Armellin, Dr Harry Holt, Dr Laura Pirovano and Prof Claudio Bombardelli
A convex-optimization-based model predictive control (MPC) algorithm for the guidance of active debris removal missions is proposed in this work. A high-accuracy reference for the convex optimization is obtained through a split- Edelbaum approach that takes the effects of J2, drag, and eclipses into account. When the spacecraft deviates significantly from the reference trajectory, a new reference is calculated through the same method to reach the target debris. When required, phasing is integrated into the transfer. During the mission, the phase of the spacecraft is adjusted to match that of the target debris at the end of the transfer by introducing intermediate waiting times. The robustness of the guidance scheme is tested in a high-fidelity dynamic model that includes thrust errors and misthrust events. The guidance algorithm performs well without requiring successive convex iterations. Monte Carlo simulations are conducted to analyze the impact of these thrust uncertainties on the guidance. Simulation results show that the proposed convex-MPC approach can ensure that the spacecraft can reach its target despite significant uncertainties and long-duration misthrust events.
Minduli Wijayatunga, Prof Roberto Armellin, Dr Harry Holt
Observability, safety, and robustness are critical for successful Rendezvous and Proximity Operation (RPO) missions. The use of Angles-only navigation (AON) for these missions is often seen as limited due to its inability to determine range, though it remains appealing for its low cost. This work uses particle swarm optimization and reinforcement learning for the trajectory design and guidance of the far-range phase of an RPO mission, ensuring observability, safety, and robustness under AON. Particle swarm optimization is used to design a nominal trajectory, and reinforcement learning is utilized to develop a guidance controller that maintains safety and observability as the spacecraft approaches its target. The constraint satisfaction challenges of particle swarm optimization and reinforcement learning are alleviated through the problem formulation, which incorporates the Lambert method to guarantee that the target state is always reached. Both the nominal trajectory and the reinforcement learning-guided trajectories are validated for observability and safety, and the guidance controller's performance is tested through Monte-Carlo simulations. Results show that for a 4 h mission previously presented in literature, in the presence of errors, the reinforcement learning controller consumes 16.58\% less Δv compared to the next-best guidance strategy explored while fully adhering to safety and observability constraints.
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