Automated design architectures for co-orbiting spacecraft swarms for planetary moon mapping
| Autor: | Ravi Teja Nallapu, Jekan Thangavelautham |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Computer science Aerospace Engineering ComputerApplications_COMPUTERSINOTHERSYSTEMS 01 natural sciences 0103 physical sciences Genetic algorithm Aerospace engineering 010303 astronomy & astrophysics 0105 earth and related environmental sciences Collision avoidance (spacecraft) Integrated design Spacecraft business.industry Swarm behaviour Astronomy and Astrophysics Spacecraft design Geophysics Space and Planetary Science Physics::Space Physics Trajectory General Earth and Planetary Sciences Astrophysics::Earth and Planetary Astrophysics business Orbit insertion |
| Zdroj: | Advances in Space Research. 67:3559-3582 |
| ISSN: | 0273-1177 |
| DOI: | 10.1016/j.asr.2020.07.014 |
| Popis: | This work describes the design and optimization of spacecraft swarm missions to meet spatial and temporal visual mapping requirements of missions to planetary moons, using resonant co-orbits. The algorithms described here are a part of Integrated Design Engineering and Automation of Swarms (IDEAS), a spacecraft swarm mission design software that automates the design trajectories, swarm, and spacecraft behaviors in the mission. In the current work, we focus on the swarm design and optimization features of IDEAS, while showing the interaction between the different design modules. In the design segment, we consider the coverage requirements of two general planetary moon mapping missions: global surface mapping and region of interest observation. The configuration of the swarm co-orbits for the two missions is described, where the participating spacecraft have resonant encounters with the moon on their orbital apoapsis. We relate the swarm design to trajectory design through the orbit insertion maneuver performed on the interplanetary trajectory using aero-braking. We then present algorithms to model visual coverage, and collision avoidance in the swarm. To demonstrate the interaction between different design modules, we relate the trajectory and swarm to spacecraft design through fuel mass, and mission cost estimations using preliminary models. In the optimization segment, we formulate the trajectory and swarm design optimizations for the two missions as Mixed Integer Nonlinear Programming (MINLP) problems. In the current work, we use Genetic Algorithm as the primary optimization solver. However, we also use the Particle Swarm Optimizer to compare the optimizer performance. Finally, the algorithms described here are demonstrated through numerical case studies, where the two visual mapping missions are designed to explore the Martian moon Deimos. |
| Databáze: | OpenAIRE |
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