6 papers · 1 filter
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection
Kyle Dunlap, Nathaniel Hamilton, Kerianne L. Hobbs
As the number of spacecraft in orbit continues to increase, it is becoming more challenging for human operators to manage each mission. As a result, autonomous control methods are…
Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection
Kyle Dunlap, Kochise Bennett, David van Wijk +2
The trial and error approach of reinforcement learning (RL) results in high performance across many complex tasks, but it can also lead to unsafe behavior. Run time assurance (RTA)…
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
Nathaniel Hamilton, Kyle Dunlap, Kerianne L. Hobbs
For many space applications, traditional control methods are often used during operation. However, as the number of space assets continues to grow, autonomous operation can enable…
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies
Kyle Dunlap, Nathaniel Hamilton, Francisco Viramontes +3
As the number of spacecraft on orbit continues to grow, it is challenging for human operators to constantly monitor and plan for all missions. Autonomous control methods such as re…
Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles
Kyle Dunlap, Nathaniel Hamilton, Zachary Lippay +3
On-orbit spacecraft inspection is an important capability for enabling servicing and manufacturing missions and extending the life of spacecraft. However, as space operations becom…
Run Time Assurance for Simultaneous Constraint Satisfaction During Spacecraft Attitude Maneuvering
Cassie-Kay McQuinn, Kyle Dunlap, Nathaniel Hamilton +2
A fundamental capability for On-orbit Servicing, Assembly, and Manufacturing (OSAM) is inspection of the vehicle to be serviced, or the structure being assembled. This research ass…