Autonomous Scheduling of Agile Spacecraft Constellations with Delay Tolerant Networking for Reactive Imaging

Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice. When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage. In prior work, we have demonstrated an algorithmic framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile, small spacecraft in a constellation. The proposed schedule optimization would run at the ground station autonomously, and the resultant schedules uplinked to the spacecraft for execution. The algorithm is generalizable over small steerable spacecraft, control capability, sensor specs, imaging requirements, and regions of interest. In this article, we modify the algorithm to run onboard small spacecraft, such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground control. We have developed a communication module based on Delay/Disruption Tolerant Networking (DTN) for onboard data management and routing among the satellites, which will work in conjunction with the other modules to optimize the schedule of agile communication and steering. We then apply this preliminary framework on representative constellations to simulate targeted measurements of episodic precipitation events and subsequent urban floods. The command and control efficiency of our agile algorithm is compared to non-agile (11.3x improvement) and non-DTN (21% improvement) constellations.

[1]  Toby D. Feaster,et al.  Methods for estimating the magnitude and frequency of floods for urban and small, rural streams in Georgia, South Carolina, and North Carolina, 2011 , 2014 .

[2]  J. Christopher Beck,et al.  Autonomous Target Search with Multiple Coordinated UAVs , 2019, J. Artif. Intell. Res..

[3]  Ziad S. Haddad,et al.  Raincube: A proposed constellation of precipitation profiling radars in CubeSat , 2014, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[4]  William D. Collins,et al.  CLARREO shortwave observing system simulation experiments of the twenty‐first century: Simulator design and implementation , 2011 .

[5]  Rainer Sandau,et al.  Small Satellite Missions for Earth Observation - New Developments and Trends , 2010 .

[6]  Steve Chien,et al.  Using Taskable Remote Sensing in a Sensor Web for Thailand Flood Monitoring , 2019, Journal of Aerospace Information Systems.

[7]  Sreeja Nag,et al.  Scheduling algorithms for rapid imaging using agile Cubesat constellations , 2017 .

[8]  Mark Abramson,et al.  Optimized Stochastic Coordinated Planning of Asynchronous Air and Space Assets , 2017, J. Aerosp. Inf. Syst..

[9]  Kerri Cahoy,et al.  Initial Results from ACCESS: An Autonomous CubeSat Constellation Scheduling System for Earth Observation , 2017 .

[10]  Vinton G. Cerf,et al.  Delay-Tolerant Networking Architecture , 2007, RFC.

[11]  Al Globus,et al.  Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach , 2002 .

[12]  Wei-Cheng Lin,et al.  Daily imaging scheduling of an Earth observation satellite , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[13]  Jeremy Frank,et al.  Scheduling Ocean Color Observations for a GEO-Stationary Satellite , 2016, ICAPS.

[14]  Mark Abramson,et al.  Earth Phenomena Observation System (EPOS) for Coordination of Asynchronous Sensor Webs , 2013 .

[15]  Fatos Xhafa,et al.  Genetic algorithms for satellite scheduling problems , 2012, Mob. Inf. Syst..

[16]  Gérard Verfaillie,et al.  Selecting and scheduling observations of agile satellites , 2002 .

[17]  Stephen Farrell,et al.  Licklider Transmission Protocol - Security Extensions , 2008, RFC.

[18]  Clifford H. Dey,et al.  Observing-Systems Simulation Experiments: Past, Present, and Future , 1986 .

[19]  Yingwu Chen,et al.  Hierarchical scheduling for real-time agile satellite task scheduling in a dynamic environment , 2019 .

[20]  Steven P. Hughes General Mission Analysis Tool (GMAT) , 2016 .

[21]  Jens Eickhoff,et al.  Onboard Computers, Onboard Software and Satellite Operations: An Introduction , 2011 .

[22]  William Martin Satellite image collection optimization , 2002 .

[23]  Frederic Teston,et al.  The PROBA/CHRIS mission: a low-cost smallsat for hyperspectral multiangle observations of the Earth surface and atmosphere , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Charles K. Gatebe,et al.  Observing system simulations for small satellite formations estimating bidirectional reflectance , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[25]  James L. Garrison,et al.  Signals of Opportunity - Airborne Demonstrator (SoOP-AD): Instrument Overview, Performance during First Flights and Future Instrument Concept [STUB] , 2018 .

[26]  Gérard Verfaillie,et al.  An earth watching satellite constellation: how to manage a team of watching agents with limited communications , 2005, AAMAS '05.

[27]  Wang Cheng,et al.  Resource planning and scheduling of payload for satellite with genetic particles swarm optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).