1,765 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
An Experimental Platform for Multi-spacecraft Phase-Array Communications
The emergence of small satellites and CubeSats for interplanetary exploration
will mean hundreds if not thousands of spacecraft exploring every corner of the
solar-system. Current methods for communication and tracking of deep space
probes use ground based systems such as the Deep Space Network (DSN). However,
the increased communication demand will require radically new methods to ease
communication congestion. Networks of communication relay satellites located at
strategic locations such as geostationary orbit and Lagrange points are
potential solutions. Instead of one large communication relay satellite, we
could have scores of small satellites that utilize phase arrays to effectively
operate as one large satellite. Excess payload capacity on rockets can be used
to warehouse more small satellites in the communication network. The advantage
of this network is that even if one or a few of the satellites are damaged or
destroyed, the network still operates but with degraded performance. The
satellite network would operate in a distributed architecture and some
satellites maybe dynamically repurposed to split and communicate with multiple
targets at once. The potential for this alternate communication architecture is
significant, but this requires development of satellite formation flying and
networking technologies. Our research has found neural-network control
approaches such as the Artificial Neural Tissue can be effectively used to
control multirobot/multi-spacecraft systems and can produce human competitive
controllers. We have been developing a laboratory experiment platform called
Athena to develop critical spacecraft control algorithms and cognitive
communication methods. We briefly report on the development of the platform and
our plans to gain insight into communication phase arrays for space.Comment: 4 pages, 10 figures, IEEE Cognitive Communications for Aerospace
Applications Worksho
Bio-Inspired Adaptive Cooperative Control of Heterogeneous Robotic Networks
We introduce a new adaptive cooperative control strategy for robotic networks comprised of heterogeneous members. The proposed feedback synchronization exploits an active parameter adaptation strategy as opposed to adaptive parameter estimation of adaptive control theory. Multiple heterogeneous robots or vehicles can coordinate their motions by parameter adaptation analogous to bio-genetic mutation and adaptation. In contrast with fixed gains used by consensus theory, both the tracking control and diffusive coupling gains are automatically computed based on the adaptation law, the synchronization errors, and the tracking errors of heterogeneous robots. The optimality of the proposed adaptive cooperative control is studied via inverse optimal control theory. The proposed adaptive
cooperative control can be applied to any network structure. The stability proof, by using a relatively new nonlinear stability tool, contraction theory, shows globally asymptotically synchronized motion of a heterogeneous robotic network. This adaptive cooperative control can be widely applied to cooperative control of unmanned aerial vehicles (UAVs), formation flying spacecraft, and multi-robot systems. Results of the simulation show the effectiveness of the proposed adaptive cooperative control laws especially for a network comprised of heterogeneous members
Optimal design for a NEO tracking spacecraft formation
The following paper presents the design and methodology for developing an optimal set of spacecraft orbits for a NEO tracking mission. The spacecraft is designed to fly in close formation with the asteroid, avoiding the nonlinear gravity field produced by the asteroid. A periodic orbit is developed, and the initial conditions are optimized by use of a global optimizer for constrained nonlinear problems. The asteroid Apophis (NEO 2004 MN4) was used as the case study due the potential impact with Earth in 2036, and the need for more accurate ephemerides
Cooperative Control with Adaptive Graph Laplacians for Spacecraft Formation Flying
This paper investigates exact nonlinear dynamics and cooperative control for spacecraft formation flying with Earth oblateness (J2 perturbation) and atmospheric drag effects. The nonlinear dynamics for chief and deputy motions are derived by using Gauss' variational equation and the Euler-Lagrangian formulation, respectively. The proposed cooperative control employs adaptive time-varying Laplacian gains. The tracking and diffusive coupling gains are adapted by the synchronization/tracking errors and distance-based connectivity, thereby defining a time-varying network topology. Moreover, the proposed method relaxes the network structure requirement and permits an unbalanced graph. Nonlinear stability is proven by contraction analysis and incremental input-to-state stability. Numerical examples show the effectiveness of the proposed method
Bio-Inspired Adaptive Cooperative Control of Heterogeneous Robotic Networks
We introduce a new adaptive cooperative control strategy for robotic networks comprised of heterogeneous members. The proposed feedback synchronization exploits an active parameter adaptation strategy as opposed to adaptive parameter estimation of adaptive control theory. Multiple heterogeneous robots or vehicles can coordinate their motions by parameter adaptation analogous to bio-genetic mutation and adaptation. In contrast with fixed gains used by consensus theory, both the tracking control and diffusive coupling gains are automatically computed based on the adaptation law, the synchronization errors, and the tracking errors of heterogeneous robots. The optimality of the proposed adaptive cooperative control is studied via inverse optimal control theory. The proposed adaptive
cooperative control can be applied to any network structure. The stability proof, by using a relatively new nonlinear stability tool, contraction theory, shows globally asymptotically synchronized motion of a heterogeneous robotic network. This adaptive cooperative control can be widely applied to cooperative control of unmanned aerial vehicles (UAVs), formation flying spacecraft, and multi-robot systems. Results of the simulation show the effectiveness of the proposed adaptive cooperative control laws especially for a network comprised of heterogeneous members
Manoeuvre Planning Architecture for the Optimisation of Spacecraft Formation Flying Reconfiguration Manoeuvres
Formation flying of multiple spacecraft collaborating toward the same goal is fast
becoming a reality for space mission designers. Often the missions require the spacecraft to
perform translational manoeuvres relative to each other to achieve some mission objective.
These manoeuvres need to be planned to ensure the safety of the spacecraft in the formation
and to optimise fuel management throughout the fleet. In addition to these requirements is it
desirable for this manoeuvre planning to occur autonomously within the fleet to reduce
operations cost and provide greater planning flexibility for the mission. One such mission that
would benefit from this type of manoeuvre planning is the European Space Agency’s
DARWIN mission, designed to search for extra-solar Earth-like planets using separated
spacecraft interferometry.
This thesis presents a Manoeuvre Planning Architecture for the DARWIN mission. The
design of the Architecture involves identifying and conceptualising all factors affecting the
execution of formation flying manoeuvres at the Sun/Earth libration point L2. A systematic
trade-off analysis of these factors is performed and results in a modularised Manoeuvre
Planning Architecture for the optimisation of formation flying reconfiguration manoeuvres.
The Architecture provides a means for DARWIN to autonomously plan manoeuvres during
the reconfiguration mode of the mission. The Architecture consists of a Science Operations
Module, a Position Assignment Module, a Trajectory Design Module and a Station-keeping
Module that represents a multiple multi-variable optimisation approach to the formation
flying manoeuvre planning problem. The manoeuvres are planned to incorporate target
selection for maximum science returns, collision avoidance, thruster plume avoidance,
manoeuvre duration minimisation and manoeuvre fuel management (including fuel
consumption minimisation and formation fuel balancing). With many customisable variables
the Architecture can be tuned to give the best performance throughout the mission duration.
The implementation of the Architecture highlights the importance of planning formation
flying reconfiguration manoeuvres. When compared with a benchmark manoeuvre planning
strategy the Architecture demonstrates a performance increase of 27% for manoeuvre
scheduling and fuel savings of 40% over a fifty target observation tour.
The Architecture designed in this thesis contributes to the field of spacecraft formation
flying analysis on various levels. First, the manoeuvre planning is designed at the mission
level with considerations for mission operations and station-keeping included in the design.
Secondly, the requirements analysis and implementation of Science Operation Module
represent a unique insight into the complexity of observation scheduling for exo-planet
analysis missions and presents a robust method for autonomously optimising that scheduling.
Thirdly, in-depth analyses are performed on DARWIN-based modifications of existing
manoeuvre optimisation strategies identifying their strengths and weaknesses and ways to
improve them. Finally, though not implemented in this thesis, the design of a Station-keeping
Module is provided to add station-keeping optimisation functionality to the Architecture
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