Space Traffic Management and the Resilient Space Environment

Introduction

The 21st century space environment offers new challenges in terms of its continued use. The increased presence of manmade objects has lead to increased risk for spacecraft as well as earth dwellers. Another potential ‘space traffic’ threat could be close passes or impacts of asteroids. Though these situations have a very rare expectation of occurrence, there is a scientific effort to prepare by studying collision avoidance options for these cases. Objects re-entering Earth’s atmosphere pose a threat to its inhabitants and property. These are man-made objects or meteorites. Not much can be done about re-entering meteorites, but it’s possible to manage the re-entering of manmade objects to some extent.

In orbit, manmade equipment faces the threat of colliding with space debris. The problem of space debris has increased in relevance since the identification of the “Kessler syndrome” in 1978, named after the NASA scientist Donald J. Kessler. It refers to the phenomenon of self-sustaining cascading collisions of space debris in Low Earth Orbit. Though this effect is restricted to the lower orbits, collision risk is also increasing in higher orbits as a result of greater utilization of the space environment by mankind.

What’s being done to address these problems?

Countering these threats requires a complex and coordinated effort. First of all, to even be aware of any potential colliders, these have to be observed and tracked. Collecting information about objects in orbit requires an infrastructure of different sensors. There is a further problem with observing these objects, though. Due to the fact that currently available sensors have a limited field of view, any given object can only be tracked during a very small fraction of its orbital revolution. This leads to the issue that it is difficult to determine which measurements correspond to the same piece of debris. It is also called the “linkage problem” and it is discussed in further detail in the chapter on Work Package 4.

It is interesting to be able to detect and predict collisions of orbital debris for the sake of being able to take preventative action as well as for having an up to date understanding of the situation. More information on detecting and avoiding collisions can be found in Work Package 1 via this link. Due to uncertainty about the position and movement of the objects in orbit, predictions about their future movement, collisions, and atmospheric re-entry worsen as one looks further in time. Quantification of uncertainty is also discussed in Work Package 1, reachable via this link. Re-entries are discussed in Work Package 2, at this link.

One of the drivers of Kessler syndrome are the large manmade objects in low orbits that will eventually collide with smaller debris or with each other, causing more fragmentation debris, which in turn increases the probability that another such event will eventually occur. To prevent this from happening, it is important to make sure that disabled equipment is removed from busy orbits or brought back into operation via servicing missions. Work Package 6, found at this link, discusses robotic and navigation technologies to interact with objects in orbit - a critical part of on-orbit servicing and debris removal missions alike.

Of course, it is necessary to regulate operation and manage traffic in space in order to ensure that the space environment remains utilizable in the future. The publication of the IAA Cosmic Study on STM 2006 defined Space traffic management (STM) as:

“The set of technical and regulatory provisions for promoting safe access into outer space, operations in outer space and return from outer space to Earth free from physical or radio-frequency interference”.

This has lead to the creation of the Guidelines for the long-term sustainability of outer space activities of UNCOPUOS and the ISO 24113:2011 Space Systems – Space Debris Mitigation standards. These standards are necessary, but not sufficient, to preserve the usability of the space environment and reduce the risk of collisions, especially in the face of the ever-increasing number and variety of orbiting objects, and the planned future large constellations.

Collision Avoidance

Advanced computational techniques may be applied to the field of operational collision avoidance, an essential mitigation strategy for protecting and managing the space environment, by:

• Predicting whether a satellite should perform a collision avoidance manoeuvre (by estimating the collision probability and other intrinsic quantities)
• Optimising planned collision avoidance manoeuvres
• Aiding in the decision making process at operator level

The Manipulation of Non-cooperative Targets and On Orbit Servicing

One of the important capabilities for space traffic management is the manipulation and removal of objects in orbit. The manipulation could be for on-orbit servicing (for cooperative satellites) or debris removal (dysfunctional satellites etc.). This process can be divided into two parts: Rendezvous and Docking for Operational and Non-Operational Targets and Robotic Manipulation.

Ultimately, many different technologies and methods need to converge for future on-orbit servicing and active debris removal activities. Some of the most difficult technical aspects of in-situ removal/servicing missions are robust navigation with respect to objects that have not been prepared to be a relative navigation target and robust physical interaction with respect to objects that have not been prepared to be handled for these purposes. For a development of state-of-the-art navigation systems that are meant to be suitable for these scenarios please refer to the page on “Rendezvous and Docking for Operational and Non-Operational Targets”. Analogous developments for physical interaction and manipulation with servicing/removal targets can be found in the page on “Robotic Manipulation”. <!—

Change Log

Following LTSII in Milano, the following content was added:

• Collision probability using differential algebra, from fragment clouds etc; collision avoidance manoeuvre optimisation;

Following LTWI in Madrid, the following content was added:

• Machine Learning using Class-Imbalanced Data techniques for class-imbalanced classification problems (contextualised in classification of chaotic motion, high risk collision events etc.) —>