Spacecraft trajectory planning using spherical expansion and sequential convex programming

Francesca Baldini, Rebecca Foust, Alexandra Bacula, Christian M. Chilan, Soon Jo Chung, Saptarshi Bandyopadhyay, Amir Rahmani, Jean Pierre De La Croix, Fred Y. Hadaegh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we develop a novel algorithm for spacecraft trajectory planning in an uncooperative cluttered environment. The Spherical Expansion and Sequential Convex Programming (SE—SCP) algorithm first uses a spherical expansion based sampling algorithm to explore the workspace. Once a path is found from the start position to the goal position, the algorithm generates a locally optimal trajectory within the homotopy class using sequential convex programming. If the number of samples goes to infinity, then the SE—SCP’s trajectory converges to the globally optimal trajectory in the workspace. The SE—SCP algorithm is computationally efficient, therefore it can be used for real-time applications on resource-constrained systems. We also present numerical simulations and comparisons with existing algorithms.

Original languageEnglish (US)
Title of host publicationAIAA/AAS Astrodynamics Specialist Conference, 2016
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624104459
StatePublished - 2016
EventAIAA/AAS Astrodynamics Specialist Conference, 2016 - Long Beach, United States
Duration: Sep 13 2016Sep 16 2016

Publication series

NameAIAA/AAS Astrodynamics Specialist Conference, 2016


OtherAIAA/AAS Astrodynamics Specialist Conference, 2016
Country/TerritoryUnited States
CityLong Beach

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Aerospace Engineering


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