3,481 research outputs found
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
Genetic algorithms for satellite scheduling problems
Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.Peer ReviewedPostprint (published version
Understanding Algorithm Performance on an Oversubscribed Scheduling Application
The best performing algorithms for a particular oversubscribed scheduling
application, Air Force Satellite Control Network (AFSCN) scheduling, appear to
have little in common. Yet, through careful experimentation and modeling of
performance in real problem instances, we can relate characteristics of the
best algorithms to characteristics of the application. In particular, we find
that plateaus dominate the search spaces (thus favoring algorithms that make
larger changes to solutions) and that some randomization in exploration is
critical to good performance (due to the lack of gradient information on the
plateaus). Based on our explanations of algorithm performance, we develop a new
algorithm that combines characteristics of the best performers; the new
algorithms performance is better than the previous best. We show how hypothesis
driven experimentation and search modeling can both explain algorithm
performance and motivate the design of a new algorithm
Mission Planning Techniques for Cooperative LEO Spacecraft Constellations
This research develops a mission planning approach that allows different systems to cooperate in accomplishing a single mission goal. Using the techniques described allows satellites to cooperate in efficiently maneuvering, or collecting images of Earth and transmitting the collected data to users on the ground. The individual resources onboard each satellite, like fuel, memory capacity and pointing agility, are used in a manner that ensures the goals and objectives of the mission are realized in a feasible way. A mission plan can be generated for each satellite within the cooperating group that collectively optimize the mission objectives from a global viewpoint. The unique methods and framework presented for planning the spacecraft operations are flexible and can be applied to a variety of decision making processes where prior decisions impact later decision options. This contribution to the satellite constellation mission planning field, thus has greater applicability to the wider decision problem discipline
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