1,765 research outputs found

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

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    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

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    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

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    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

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    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

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    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

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    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

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    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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