STPSat-1: A New Approach to DoD Experiment Spaceflight

For small satellites, finding affordable access to space is a daunting hurdle. The Evolved Expendable Launch Vehicle (EELV) Secondary Payload Adapter (ESPA) promises to make excess capacity on future EELV launches available for the Department of Defense (DoD) Space Test Program (STP) and other organizations as a lower-cost launch alternative. STP Satellite Mission 1 (STPSat-1) is the first STP satellite built specifically to exploit this capacity. STPSat-1 continues STP’s mission to provide access to space for DoD sponsored experiments. This spacecraft hosts four such experiments: Spatial Heterodyne Imager for Mesospheric Radicals (SHIMMER); Computerized Ionospheric Tomography Receiver in Space (CITRIS); Micro-Electro-Mechanical System (MEMS)-based PicoSat Inspector (MEPSI), and; Wafer Scale Signal Processing (WSSP). Consistent with STP’s mission, these experiments will demonstrate new technologies for space applications. This paper discusses several technical challenges being overcome by the STPSat-1 team. SHIMMER is the primary driver for spacecraft attitude and thermal performance. ESPA restrictions tightly constrain volume and mass. Limited knowledge of the launch environment exists since Delta IV has not yet flown (at this writing). This paper will discuss the approach used to meet these technical challenges, present organizational structures used to optimize communications, and address design-to-cost and mission risk constraints

Utilizing AI in Temporal, Spatial, and Resource Scheduling

Aurora is a software system enabling the rapid, easy solution of complex scheduling problems involving spatial and temporal constraints among operations and scarce resources (such as equipment, workspace, and human experts). Although developed for use in the International Space Station Processing Facility, Aurora is flexible enough that it can be easily customized for application to other scheduling domains and adapted as the requirements change or become more precisely known over time. Aurora s scheduling module utilizes artificial-intelligence (AI) techniques to make scheduling decisions on the basis of domain knowledge, including knowledge of constraints and their relative importance, interdependencies among operations, and possibly frequent changes in governing schedule requirements. Unlike many other scheduling software systems, Aurora focuses on resource requirements and temporal scheduling in combination. For example, Aurora can accommodate a domain requirement to schedule two subsequent operations to locations adjacent to a shared resource. The graphical interface allows the user to quickly visualize the schedule and perform changes reflecting additional knowledge or alterations in the situation. For example, the user might drag the activity corresponding to the start of operations to reflect a late delivery

A flight software development and simulation framework for advanced space systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2002.Includes bibliographical references (p. 293-302).Distributed terrestrial computer systems employ middleware software to provide communications abstractions and reduce software interface complexity. Embedded applications are adopting the same approaches, but must make provisions to ensure that hard real-time temporal performance can be maintained. This thesis presents the development and validation of a middleware system tailored to spacecraft flight software development. Our middleware runs on the Generalized Flight Operations Processing Simulator (GFLOPS) and is called the GFLOPS Rapid Real-time Development Environment (GRRDE). GRRDE provides publish-subscribe communication services between software components. These services help to reduce the complexity of managing software interfaces. The hard real-time performance of these services has been verified with General Timed Automata modelling and extensive run-time testing. Several example applications illustrate the use of GRRDE to support advanced flight software development. Two technology-focused studies examine automatic code generation and autonomous fault protection within the GRRDE framework. A complex simulation of the TechSat 21 distributed spacebased radar mission highlights the utility of the approach for large-scale applications.by John Patrick Enright.Ph.D

Integrated System for Autonomous Science

The New Millennium Program Space Technology 6 Project Autonomous Sciencecraft software implements an integrated system for autonomous planning and execution of scientific, engineering, and spacecraft-coordination actions. A prior version of this software was reported in "The TechSat 21 Autonomous Sciencecraft Experiment" (NPO-30784), NASA Tech Briefs, Vol. 28, No. 3 (March 2004), page 33. This software is now in continuous use aboard the Earth Orbiter 1 (EO-1) spacecraft mission and is being adapted for use in the Mars Odyssey and Mars Exploration Rovers missions. This software enables EO-1 to detect and respond to such events of scientific interest as volcanic activity, flooding, and freezing and thawing of water. It uses classification algorithms to analyze imagery onboard to detect changes, including events of scientific interest. Detection of such events triggers acquisition of follow-up imagery. The mission-planning component of the software develops a response plan that accounts for visibility of targets and operational constraints. The plan is then executed under control by a task-execution component of the software that is capable of responding to anomalies

Research Naval Postgraduate School, v.12, no.3, October 2002

NPS Research is published by the Research and Sponsored Programs, Office of the Vice President and Dean of Research, in accordance with NAVSOP-35. Views and opinions expressed are not necessarily those of the Department of the Navy.Approved for public release; distribution is unlimited

On-Orbit Demonstrations of Robust Autonomous Operations on Cubesat

As we accumulate experiences of satellite developments, we clearly recognize the importance of successful operations and difficulty to achieve them. There are many anomalous events in orbit especially for small satellites. It is costly or impossible to consider all anomalies in advance. The autonomous operation functions, we have developed, can operate the satellite without operators and achieve operation intents. The functions have the satellite behavior (state) models and the given operation intents. They generate the on-board operation procedures from the behavior models and execute them. Even if the status may not transit as expected due to anomalies, they can re-recognize the new status, generate the operation procedures again, and achieve the operation intents robustly. We have demonstrated the autonomous operation functions on a 3U CubeSat called TRICOM-1R that was launched by the newly developed and dedicated small satellite launcher SS-520 on 3rd Feb. 2018. The autonomous functions worked correctly and tried turning on the cameras without any predetermined operation procedures during the very first cycle of the orbit. The demonstration of them has successfully completed. We have several CubeSats and small satellites now in development and we will implement the upgraded version of the autonomous functions on them

Distributed Control of Servicing Satellite Fleet Using Horizon Simulation Framework

On-orbit satellite servicing is critical to maximizing space utilization and sustainability and is of growing interest for commercial, civil, and defense applications. Reliance on astronauts or anchored robotic arms for the servicing of next-generation large, complex space structures operating beyond Low Earth Orbit is impractical. Substantial literature has investigated the mission design and analysis of robotic servicing missions that utilize a single servicing satellite to approach and service a single target satellite. This motivates the present research to investigate a fleet of servicing satellites performing several operations for a large, central space structure. This research leverages a distributed control approach, implemented using the Horizon Simulation Framework (HSF), to develop a tool capable of integrated mission modeling and task scheduling for a servicing satellite fleet. HSF is a modeling and simulation framework for verification of system level requirements with an emphasis on state representations, modularity, and event scheduling. HSF consists of two major modules: the main scheduling algorithm and the system model. The distributed control architecture allocates processing and decision making for this multi-agent cooperative control problem across multiple subsystem models and the main HSF scheduling algorithm itself. Models were implemented with a special emphasis on the dynamics, control, trajectory constraints, and trajectory optimization for the servicing satellite fleet. The integrated mission modeling and scheduling tool was applied to a sample scenario in which a fleet of 3 servicing assets is tasked with performing 12 servicing activities for a large satellite in Geostationary Orbit. The tool was able to successfully determine a schedule in which all 12 servicing activities were completed in under 32 hours, subject to the fuel and trajectory constraints of the servicing assets