Session
Weekend Session V: Automation - Research and Academia
Location
Utah State University, Logan, UT
Abstract
The ITA Space Center (CEI) is dedicated to advancing space systems and cultivating expertise in the sector. In pursuit of these objectives, CEI has initiated CubeSat development projects, commencing with the ITASAT mission launched in December 2018 and followed by the successful deployment of the SPORT satellite project in 2022. ITASAT's primary purpose was to be a hands-on project, from design to operation. SPORT is designed to study space weather, specifically focusing on the dynamics of plasma bubble formation in the ionosphere near-equatorial regions. Acknowledging the inherent challenges posed by power budget limitations, it became apparent that continuous scientific data collection was unattainable. As a result, a strategic decision was made to implement a power management strategy, composed of power monitoring and a science scheduler incorporated into the satellite's infrastructure. Currently, CEI is actively developing the ITASAT2 mission—a constellation comprising three 12U satellites. The mission aims to extend the scientific investigation initiated by the SPORT mission, enhancing temporal resolution and expanding objectives to include ground-based RF source geolocation. Despite persistent power limitations, the power management strategy employed in the SPORT mission with the integration of power monitoring and science scheduler is crucial for optimal resource management for the ITASAT2 mission. The ITASAT2 mission has increased in complexity due to the introduction of in-flight formation operations and geolocation technology demonstrations. Addressing these requirements, and drawing on the lessons learned from the SPORT mission, a proactive approach is being pursued to explore the development of autonomous operations. This paper proposes an investigation of possible strategies for automating ITASAT2 mission operations to maximize satellite resources for collecting and delivering scientific data.
An Investigation of Automation Strategies to Optimize ITASAT2 Mission Operations
Utah State University, Logan, UT
The ITA Space Center (CEI) is dedicated to advancing space systems and cultivating expertise in the sector. In pursuit of these objectives, CEI has initiated CubeSat development projects, commencing with the ITASAT mission launched in December 2018 and followed by the successful deployment of the SPORT satellite project in 2022. ITASAT's primary purpose was to be a hands-on project, from design to operation. SPORT is designed to study space weather, specifically focusing on the dynamics of plasma bubble formation in the ionosphere near-equatorial regions. Acknowledging the inherent challenges posed by power budget limitations, it became apparent that continuous scientific data collection was unattainable. As a result, a strategic decision was made to implement a power management strategy, composed of power monitoring and a science scheduler incorporated into the satellite's infrastructure. Currently, CEI is actively developing the ITASAT2 mission—a constellation comprising three 12U satellites. The mission aims to extend the scientific investigation initiated by the SPORT mission, enhancing temporal resolution and expanding objectives to include ground-based RF source geolocation. Despite persistent power limitations, the power management strategy employed in the SPORT mission with the integration of power monitoring and science scheduler is crucial for optimal resource management for the ITASAT2 mission. The ITASAT2 mission has increased in complexity due to the introduction of in-flight formation operations and geolocation technology demonstrations. Addressing these requirements, and drawing on the lessons learned from the SPORT mission, a proactive approach is being pursued to explore the development of autonomous operations. This paper proposes an investigation of possible strategies for automating ITASAT2 mission operations to maximize satellite resources for collecting and delivering scientific data.
