Review of Formation Flying and Constellation Missions Using Nanosatellites

Bandyopadhyay, Saptarshi; Foust, Rebecca; Subramanian, Giri P.; Chung, Soon‐Jo; Hadaegh, Fred Y.

doi:10.2514/1.a33291

Cited by 191 publications

(88 citation statements)

References 21 publications

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“…The aforementioned conditions are conclusive in the absence of other dynamical terms like (4). The passivity of the input-output dynamics [67]- [69] is commonly used to analyze the stability of networked nonlinear systems that have both the Laplacian matrix (L in (4)) and the nonlinear dynamical terms (e.g., convection terms of (3) or the Lagrangian form in (5)).…”

Section: F Synchronization and Hierarchical Stability For Swarmsmentioning

confidence: 99%

“…If a linear diffusive coupling is used, τ i would produce L similar to (4). The EulerLagrange equations appear routinely robotics in the study of rigid body motions of manipulators [26], [27] and spacecraft or aircraft (SE(3)), which have attitude dynamics on SO(3) [28]- [30] and often times have articulated wings [14], [31], [32], appendages, or manipulators attached:…”

Section: Physics-based Models For Robotic Agentsmentioning

confidence: 99%

“…Our survey paper also addresses the challenges of integrating autonomous aerial swarm systems with other types of robots, such as ground vehicles. From a technological standpoint, the broader impacts of research in aerial swarm robotics include scalability and down-compatibility with 2-D robotic networks (e.g., ground robots) and other 3-D unmanned systems such as spacecraft swarms [4], [5] and underwater swarms [6]. The distinguishing characteristics of aerial swarm robotics are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: F Synchronization and Hierarchical Stability For Swarmsmentioning

confidence: 99%

Section: Physics-based Models For Robotic Agentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey on Aerial Swarm Robotics

et al. 2018

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“…[2][3][4][5][6][7] Although there have been significant advances in the development of swarm guidance algorithms for cooperative spacecraft, they cannot be directly applied to handle static and time-varying uncooperative obstacles. In this paper, we present a novel guidance algorithm for spacecraft swarms in an active environment cluttered with many time-varying, moving obstacles, like a debris field or the asteroid belt, and desired time-varying terminal positions.…”

Section: Introductionmentioning

confidence: 99%

Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments

Bandyopadhyay

Baldini

Foust³

et al. 2017

AIAA SPACE and Astronautics Forum and Exposition

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This paper focuses on trajectory planning for spacecraft swarms in cluttered environments, like debris fields or the asteroid belt. Our objective is to reconfigure the spacecraft swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with the obstacles. In our prior work we proposed a novel distributed guidance algorithm for spacecraft swarms in static environments.1 In this paper, we present the Multi-Agent Moving-Obstacles Spherical Expansion and Sequential Convex Programming (MAMO SE-SCP) algorithm that extends our prior work to include spatiotemporal constraints such as time-varying, moving obstacles and desired time-varying terminal positions. In the MAMO SE-SCP algorithm, each agent uses a spherical-expansion-based sampling algorithm to cooperatively explore the time-varying environment, a distributed assignment algorithm to agree on the terminal position for each agent, and a sequential-convex-programming-based optimization step to compute the locally-optimal trajectories from the current location to the assigned time-varying terminal position while avoiding collision with other agent and the moving obstacles. Simulations results demonstrate that the proposed distributed algorithm can be used by a spacecraft swarm to achieve a time-varying, desired formation around an object of interest in a dynamic environment with many moving and tumbling obstacles.

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“…This increase in the last years of science missions in LEO orbits can be seen in; small satellites projects which integrate CubeSat platform units, such as FIREBIRD placed into orbit by the Delta II rocket to study the Van Allen belts radiation (Crew et al 2015), and Centennial placed into orbit by the ISS to validate a satellite monitoring system from Earth; initiatives of CubeSat constellations (Conklin et al 2013;Leiter, 2013;Subramanian et al 2015;Peral et al 2015;Bandyopadhyay et al 2016), and; experimental studies in CubeSat performances and specific applications (Jones, 2013;Cartwright, 2014;Venturini, 2014;Briggs et al 2015;Fields et al 2015;Westerhoff et al 2015;Carreno-Luengo et al 2016). In addition, the last report …”

Section: Leo Orbits Missionsmentioning

confidence: 99%

Analysis Model to Optimize Ground Stations in Built-up Areas

Chinchilla¹

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Review of Formation Flying and Constellation Missions Using Nanosatellites

Cited by 191 publications

References 21 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments

Analysis Model to Optimize Ground Stations in Built-up Areas

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