D-Shield: Distributed Spacecraft With Heuristic Intelligence to Enable Logistical Decisions

Nag, Sreeja; Moghaddam, Mahta; Selva, Daniel; Frank, Jeremy; Ravindra, Vinay; Levinson, Richard; Azemati, Amir; Aguilar, Alan; Li, Alan; Akbar, Ruzbeh

doi:10.1109/igarss39084.2020.9323248

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…Given a desired attitude pointing schedule {𝑡 𝑘 , q(𝑡 𝑘 ), ω(𝑡 𝑘 )} ∀ 𝑘 ∈ K, we may plan a continuous trajectory that passes through each attitude point in the sequence by using Minimum-Effort Multi-Target Pointing OCP (Section III.B). To motivate this method, we consider the remote-sensing application described in [2], where a 24-satellite, 3-plane Walker-Delta constellation is simulated at 710 km altitude, 98.5 deg inclined, circular orbits over a 6-hour duration with the orbital mechanics module of the D-SHIELD software suite [4]. Simulation results include the orbital states of the satellites at each time step as well as access times when user-defined target grid points (described by lattitude and longitude coordinates) are observable by a satellite.…”

Section: B Minimum-effort Multi-target Pointing Ocp Examplementioning

confidence: 99%

“…An optimization-based scheduler may explicitly use the minimum maneuver time model described in Section V.A to accurately estimate the fastest transition time between GP observations. By finding the most time-efficient sequence of GPs, the scheduler can maximize the number of observations or maximize science utility [4].…”

Section: Fig 3 (Left) Earth-centered Inertial (Right) Earth-centered ...mentioning

confidence: 99%

“…LEO constellations may also be employed to measure episodic precipitation and subsequent water flow in flood-prone cities [2]- [3]. Furthermore, constellations may be tasked to measure soil moisture in targeted regions to assess the risk of wildfires [4]. In addition to climate and environment monitoring, Earth-observing constellations are providing commercial and economic value by measuring, for instance, agricultural crop growth, infrastructure development, or logistical activity at airports and shipping container routes [5].…”

Section: Introductionmentioning

confidence: 99%

“…In the subsequent operational stage, the satellites must perform various scheduled tasks including targeted remote sensing (e.g., imaging, radiometry), downlinking/uplinking data to/from ground stations, orbital station-keeping and other maintenance activities. In [1]- [4], an automated scheduler is developed to run autonomously on either ground stations (with schedules uplinked to satellites) or onboard in a distributed approach. Based on dynamic programming or mixed integer programming, the scheduler produces imaging schedules for each satellite that maximizes the number of observations and/or observation times for the constellation as a whole.…”

Section: Introductionmentioning

confidence: 99%

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Attitude Trajectory Optimization for Agile Satellites in Autonomous Remote Sensing Constellation

Sin¹,

Nag²,

Ravindra³

et al. 2021

Preprint

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Agile attitude maneuvering maximizes the utility of remote sensing satellite constellations. By taking into account a satellite's physical properties and its actuator specifications, we may leverage the full performance potential of the attitude control system to conduct agile remote sensing beyond conventional slew-and-stabilize maneuvers. Employing a constellation of agile satellites, coordinated by an autonomous and responsive scheduler, can significantly increase overall response rate, revisit time and global coverage for the mission. In this paper, we use recent advances in sequential convex programming (SCP) based trajectory optimization to enable rapid-target acquisition, pointing and tracking capabilities for a scheduler-based constellation. We present two problem formulations. The Minimum-Time Slew Optimal Control Problem determines the minimum time, required energy, and optimal trajectory to slew between any two orientations given nonlinear quaternion kinematics, gyrostat and actuator dynamics, and state/input constraints. By gridding the space of 3D rotations and efficiently solving this problem on the grid, we produce lookup tables or parametric fits off-line that can then be used on-line by a scheduler to compute accurate estimates of minimum-time and maneuver energy for real-time constellation scheduling. The estimates allow an optimization-based scheduler to produce target-remote-sensing and data-downlinking schedules that are dynamically feasible for each satellite and optimal for the constellation. The Minimum-Effort Multi-Target Pointing Optimal Control Problem is used on-line by each satellite to produce continuous attitude-state and control-input trajectories that realize a given schedule while minimizing attitude error and control effort. The optimal trajectory may then be achieved by a low-level tracking controller. This onboard trajectory generation and tracking scheme is possible due to realtime, efficient SCP implementations. We demonstrate our approach with a numerical example that uses simulation data for a reference satellite in Sun-synchronous orbit passing over globallydistributed, Earth-observation targets.

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Section: B Minimum-effort Multi-target Pointing Ocp Examplementioning

confidence: 99%

Section: Fig 3 (Left) Earth-centered Inertial (Right) Earth-centered ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Attitude Trajectory Optimization for Agile Satellites in Autonomous Remote Sensing Constellation

Sin¹,

Nag²,

Ravindra³

et al. 2021

Preprint

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“…The D-SHIELD application (Levinson, et al, 2021, Nag et al, 20202019) uses a (proposed) constellation of satellites looking at Earth to reduce errors in global soil moisture prediction by making observations that target spatio-temporal points of rising prediction error.…”

mentioning

confidence: 99%

Planning Satellite Swarm Measurements for Earth Science Models: Comparing Constraint Processing and MILP Methods

Levinson¹,

Niemoeller²,

Nag³

et al. 2022

ICAPS

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We compare two planner solutions for a challenging Earth science application to plan coordinated measurements (observations) for a constellation of satellites. This problem is combinatorially explosive, involving many degrees of freedom for planner choices. Each satellite carries two different sensors and is maneuverable to 61 pointing angle options. The sensors collect data to update the predictions made by a high-fidelity global soil moisture prediction model. Soil moisture is an important geophysical variable whose knowledge is used in applications such as crop health monitoring and predictions of floods, droughts, and fires. The global soil-moisture model produces soil-moisture predictions with associated prediction errors over the globe represented by a grid of 1.67 million Ground Positions (GPs). The prediction error varies over space and time and can change drastically with events like rain/fire. The planner's goal is to select measurements which reduce prediction errors to improve future predictions. This is done by targeting high-quality observations at locations of high prediction-error. Observations can be made in multiple ways, such as by using one or more instruments or different pointing angles; the planner seeks to select the way with the least measurement-error (higher observation quality). In this paper we compare two planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. DCP uses domain heuristics to find solutions within a reasonable time for our application but cannot be proven optimal, while the MILP produces provably optimal solutions. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.

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New observing strategies testbed: A digital prototyping platform for distributed space missions

Chell

Levine

Capra

et al. 2023

Systems Engineering

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The New Observing Strategies Testbed (NOS‐T) is a digital engineering environment for enabling distributed space mission (DSM) technology demonstrations. Its event‐driven architecture enables users to orchestrate DSM test campaigns by developing applications that communicate state changes via messages. NOS‐T is motivated by requirements such as geographical distribution, cross‐boundary participation, wide applicability, and usability that make it unique in this field. This article introduces NOS‐T and describes its architecture in the context of an example DSM test suite, FireSat+. The scalability of NOS‐T is demonstrated with a performance assessment of its capabilities under a stress test of high message frequency and payload size, which are both related to the complexity of potential user‐generated test cases. Results show that message periodicity has no significant effect on median delay time over the ranges sampled; however, the message payload size induces linear growth in median delay time of approximately 1.5 ms per kB. Future NOS‐T applications can adjust the execution time scaling factor and message payload size to match operational constraints on allowable delay.

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D-Shield: Distributed Spacecraft With Heuristic Intelligence to Enable Logistical Decisions

Cited by 8 publications

References 5 publications

Attitude Trajectory Optimization for Agile Satellites in Autonomous Remote Sensing Constellation

Attitude Trajectory Optimization for Agile Satellites in Autonomous Remote Sensing Constellation

Planning Satellite Swarm Measurements for Earth Science Models: Comparing Constraint Processing and MILP Methods

New observing strategies testbed: A digital prototyping platform for distributed space missions

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