On-board emergent scheduling of autonomous spacecraft payload operations

This paper describes a behavioral competency level concerned with emergent scheduling of spacecraft payload operations. The level is part of a multi-level subsumption architecture model for autonomous spacecraft, and it functions as an action selection system for processing a spacecraft commands that can be considered as 'plans-as-communication'. Several versions of the selection mechanism are described, and their robustness is qualitatively compared

CubeSat Astronomy Mission Modeling Using the Horizon Simulation Framework

The CubeSat Astronomy Network is a proposed system of multiple CubeSat spacecraft capable of performing follow-up observations of astronomical targets of interest. The system is intended to serve as a space-borne platform that can complement existing systems utilized for astronomical research by undergraduate and high school students. Much research and development work has been performed to develop model-based system engineering methodologies and products for CubeSat missions, including the Horizon Simulation Framework. The Horizon Simulation Framework enables the development of system models using the Extended Markup Language (XML), and its simulation program can generate system simulations over model-specified timespans. System requirements and constraints, as well as subsystem dependencies and functions, can also be directly specified in these models. Previous work using the framework has been performed to characterize “day-in-the-life” operations for Earth-observing spacecraft. A similar goal is intended for modeling the CubeSat Astronomy Network: simulating mission operations during nominal conditions to validate system and subsystem requirements. By developing this model, system and subsystem requirements derived in the course of preliminary design for the Network can be analyzed, modelled, and evaluated for feasibility. These results can then be used to inform design decisions related to system architecture and concept of operations at the early stages of design, while the models themselves can grow and mature alongside project development and be re-used for future design work

Enabling Mission Success: A Student\u27s Perspective on Developing a Ground Station

In recent years, The University of Colorado Boulder’s Laboratory for Atmospheric and Space Physics (LASP) has grown its SmallSat Operations (SMOPS) program into a large team of students and professionals managing the operations of multiple SmallSat missions. The team responsible for the on-site ground station (GSOPS) includes a small team of students alongside professionals who help manage the on-site UHF and S-band antennas, making use of open-source, commercial, and in-house software. In this process, the student ground station team has adapted the limited time and resources of a single ground station to accommodate the needs and science objectives of multiple missions. These accommodations include the development of best practices for scheduling around pass overlap, the design of automation capable of handling multiple missions without manual reconfiguration, and the implementation of robust error handling and intuitive paging to reduce the need for human intervention. This paper describes in more detail the students’ perspective on the challenges and creative solutions of maintaining and improving the ground station

Enhancing spaceflight safety with UOS3 cubesat

Earth orbits are becoming increasingly congested. This will not only impact future space operations but also become a concern for the population on the ground; with more spacecraft being flown, more objects will re-enter the atmosphere in an uncontrolled fashion. Parts of these satellites can reach Earth surface and endanger the ground population (e.g. ROSAT or UARS satellites). A student-run project from the University of Southampton aims to build a 1U cubesat (approx. 10 by 10 by 10 cm satellite), which will gather data that will improve the accuracy of re-entry predictions. The cubesat will record and deliver its position and attitude during the orbital decay, thus providing validation data for re-entry prediction tools. This will reduce the risk to the ground population because more accurate prognoses will allow mitigation measures to be implemented in the areas at risk. The mission could also allow the risk of collision between spacecraft to be estimated more accurately thanks to improvement of the atmospheric models. This would give the decision makers more complete information to use, for instance, in collision avoidance manoeuvre plannin

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

Conceptual Design and Analysis of Service Oriented Architecture (SOA) for Command and Control of Space Assets

The mission-unique model that has dominated the DoD satellite Command and Control community is costly and inefficient. It requires repeatedly “reinventing” established common C2 components for each program, unnecessarily inflating budgets and delivery schedules. The effective utilization of standards is scarce, and proprietary, non-open solutions are commonplace. IT professionals have trumpeted Service Oriented Architectures (SOAs) as the solution to large enterprise situations where multiple, functionally redundant but non-compatible information systems create large recurring development, test, maintenance, and tech refresh costs. This thesis describes the current state of Service Oriented Architectures as related to satellite operations and presents a functional analysis used to classify a set of generic C2 services. By assessing the candidate services’ suitability through a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, several C2 functionalities are shown to be more ready than others to be presented as services in the short term. Lastly, key enablers are identified, pinpointing the necessary steps for a full and complete transition from the paradigm of costly mission-unique implementations to the common, interoperable, and reusable space C2 SOA called for by DoD senior leaders

Spacecraft Mission Agent for Autonomous Robust Task Execution

Autonomy in space systems can drastically reduce the workload of ground crews for satellite missions, especially for clusters of satellites. Additionally, autonomy can increase the efficiency of missions by maximizing the utilization of resources and rapidly handling any issues that arise without having to wait for instructions from the ground. This research presents an agent-based, task-execution approach to onboard spacecraft autonomy. Instead of the traditional approach requiring onboard planning and scheduling, this method uses a combination of constraint and priority parameters associated with every task to ensure robust task execution with behavior as intended. Using this method, tasks will only run under safe conditions (e.g. no conflict with any running tasks), which enables conflicting tasks to be scheduled closer together or even overlapping for lower-priority tasks. This approach manages the execution of tasks on the timescale of seconds, enabling conflicting tasks to run sequentially, thereby increasing productivity if earlier tasks finish ahead of schedule. This framework leverages the NASA-developed, open-source projects cFE and PLEXIL and was tested on development boards comparable to flight hardware

A comparative analysis of algorithms for satellite operations scheduling

Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration