The threat posed by space debris to operational satellites is becoming a serious problem. Currently there are nearly 20,000 catalogued objects larger than 10cm in LEO and 1m in GEO and more than twice as many smaller fragments down to 1cm.
America’s 18th Space Control Squadron (18 SPCS, formerly JSpOC) issues conjunction data messages (CDMs) whenever an upcoming conjunction is detected between an operational satellite and the catalogued population. These messages are the main source of information for satellite operators to perform dedicated collision avoidance maneuvers in order to reduce the collision risk below an acceptable level.
These critical operations are generally difficult to automate and usually lead to stressful situations involving decision-making procedures that have to take into account a large number of factors and different sources of information.
When dealing with small satellite fleets or orbiting in regions not densely populated, the number of alerts can be manageable by means of well-established procedures and intensive manual operations. In the case of large fleets, however. and particularly when operating in crowded orbital regions, the number of alerts would soar to almost unmanageable levels.
This implies the need to automate, at least partially, such operations in order to increase safety while limiting operational costs.
However, the collision avoidance decision-making procedure (whether to affect a maneuver or not) is not simple to automate; the decision cannot be taken on the basis of a single algorithm implementing simple rules on the input data (mainly directly from the CDMs or derived from them).
Nonetheless, there is a significant amount of past experience to draw on while performing these operations, and well-trained operators could make their decisions accordingly (with sufficient time for analysis). This information, drawn from decisions made in real or simulated scenarios, could hence be used as generic algorithm-training data. Here is where the concepts of artificial intelligence and, particularly, machine learning come into play.
On June 3 I gave a webinar about the applicability of AI & ML technologies to the automatic collision avoidance process and the latest developments in the domain. GMV is developing an Autonomous Collision Avoidance System along with EUTELSAT within the ESA project AUTOCA. This system will be based on the use of AI and ML techniques and is designed for large-fleet use (e.g. large operators in GEO and future mega-constellations in LEO and MEO) and also orbit raising scenarios with full-electric satellites (e.g. orbit transfer from LEO to Upper-LEO for deployment of a large constellation or from LEO/GTO to GEO for a large telecom satellite).
During the webinar we discussed the application of AI/ML technology not only to this particular problem but also to other space applications such as satellite-operation automation, satellite communications, robotics and onboard automation, Earth observation data processing, etc. where GMV is also working on a large number of activities. I also emphasized the vast experience of GMV’s IT branch in AI, Big Data and Data Science technology; this has enabled us to exploit synergies and bring these technologies to the space and defense domains.
It was a very interesting chat and I would like to thank my GMV colleagues working on this subject (check out the first slide!) as well as all those who attended the webinar, and particularly for the interesting questions raised.
In the following link you can watch the complete webinar, in case you missed it. Feel free to contact me (email provided below and in the presentation) if you are interested in the subject and want to share some ideas or seek collaboration. Thanks!
Author: Alberto Águeda
Las opiniones vertidas por el autor son enteramente suyas y no siempre representan la opinión de GMV
The author’s views are entirely his own and may not reflect the views of GMV