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The main objective of AUTOCA is the development of autonomous collision avoidance mechanisms, consisting of approach and software, for NGSO constellations during routine operations and for satellites’ operations during electric orbit raising or lowering. Therefore, AUTOCA allows a considerable reduction of the operational load associated to collision avoidance operations.
In addition, AUTOCA aims to enable key technology developments for autonomous COLA operations not available today. Moreover, the objective includes reducing operation load and increase the safety of operations in order to preserve the space environment.
AUTOCA faces several challenges. The biggest challenge is represented by the lack of historical data in relation to COLA. The number of close encounters is increasing. However, the available data, in form of CDMs, consists of many uneventful conjunctions. Only a small fraction of close encounters lead to a manoeuvre being executed. Moreover, the decision took for each conjunction can vary, depending on the operator. Thus, an inconsistency in historical decisions must be taken into account.
In the case of impulsive propulsion, data is more prevalent compared with electric propulsion scenario. This added lack of data for electric propulsion is the second highest challenge.
Space exploration and utilisation is becoming one of the main industries of the world. The population of satellites is predicted to increase even further. Large and mega constellations have already been planned. The resulting increase in space population leads to a higher number of close approaches between space objects. Therefore, the large population may cause a substantial number of warnings, which render impractical and error-prone a human-based approach.
AUTOCA has the potential to mitigate this problem. Using an automated system, AUTOCA offers a reliable solution to the analysis of close approaches. Using historical decisions, along with a state of the art machine learning algorithm, AUTOCA provides consistent in-time warnings for operators. Additionally, the system can train with an operator’s data, such that the output accurately projects the operator’s activity.
Several features have been defined, as identified by each major component that is considered in the general architecture.
Generation of a varied array of outputs, including predicted status for the CDM, covariance plots, evolution of probability, etc.
AUTOCA is designed as modular. Each part of the project is to be designed as separate component to a high degree in order to facilitate integrating the building blocks in other software environments.
The following high-level components have been identified in the software:
The project spans over a period of 24 months, with the activities divided into three main phases:
Main phases, work packages and milestones are described in the image below:
The project is currently at the end of the first phase of the Study/Prototyping stage. A first assessment of the different machine learning algorithms suitable to be applied for the autonomous COLA has been delivered for the first Milestone of the project. The next activities to start involve a more in-depth analysis on the algorithms, a selection of the most promising approaches, and their prototyping.