Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Dawson Jones, Senior Rogelio Gracia Otalvaro, Graduate Student
Lead Presenter's Name
Dawson Jones
Lead Presenter's College
DB College of Aviation
Faculty Mentor Name
Bryan Watson
Abstract
The space economy is projected to grow to 1.8 trillion USD by 2030, according to the World Economic Forum and McKinsey & Company. As space operations become more common and diversified, the hazardous nature of the environment is worsened by the growing number of complex actors. Currently, ground segments manage these hazards and risks, but they are faced with potential operation challenges such as communications delays, communication issues due to single event upsets, and orbital perturbations. Additional environmental challenges that further complicate these operations might be satellite collisions, anti-satellite weaponry, orbital debris, and uncontrolled space. Furthermore, the increasing number of actors in space is likely to outpace the growth of personnel in this field and the infrastructure to support them. To address these challenges, one possible solution is the use of machine learning (ML) solutions in space to reduce the personnel needed and infrastructure to support them and increase autonomy. A key innovation in this field would be dynamic automated spacecraft coupling. In these scenarios, automated models would determine the optimal approach, implement the necessary actions to couple with the target object, and be capable of aborting if anomalies compromise the safety of the coupling. This study seeks to determine the viability of such a model using Proximal Policy Optimization along R-bar, V-bar, and Z-bar approaches in a 6-degree-of-freedom environment, implementing abort scenarios based on a predefined keep-out zone around the target objective.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Machine Learning for Near-Proximity Operations
The space economy is projected to grow to 1.8 trillion USD by 2030, according to the World Economic Forum and McKinsey & Company. As space operations become more common and diversified, the hazardous nature of the environment is worsened by the growing number of complex actors. Currently, ground segments manage these hazards and risks, but they are faced with potential operation challenges such as communications delays, communication issues due to single event upsets, and orbital perturbations. Additional environmental challenges that further complicate these operations might be satellite collisions, anti-satellite weaponry, orbital debris, and uncontrolled space. Furthermore, the increasing number of actors in space is likely to outpace the growth of personnel in this field and the infrastructure to support them. To address these challenges, one possible solution is the use of machine learning (ML) solutions in space to reduce the personnel needed and infrastructure to support them and increase autonomy. A key innovation in this field would be dynamic automated spacecraft coupling. In these scenarios, automated models would determine the optimal approach, implement the necessary actions to couple with the target object, and be capable of aborting if anomalies compromise the safety of the coupling. This study seeks to determine the viability of such a model using Proximal Policy Optimization along R-bar, V-bar, and Z-bar approaches in a 6-degree-of-freedom environment, implementing abort scenarios based on a predefined keep-out zone around the target objective.