Collision Avoidance Maneuver Optimization: A Fast and Accurate Semi-analytical Approach

Hernando Ayuso, Javier (2014). Collision Avoidance Maneuver Optimization: A Fast and Accurate Semi-analytical Approach. Thesis (Master thesis), E.T.S.I. Aeronáuticos (UPM).

Description

Title: Collision Avoidance Maneuver Optimization: A Fast and Accurate Semi-analytical Approach
Author/s:
  • Hernando Ayuso, Javier
Contributor/s:
  • Bombardelli, Claudio
Item Type: Thesis (Master thesis)
Masters title: Ingeniería Aeroespacial
Date: September 2014
Subjects:
Freetext Keywords: Collision avoidance, Collision Probability, Optimization, OCCAM, Orbit Uncertainty
Faculty: E.T.S.I. Aeronáuticos (UPM)
Department: Física Aplicada a la Ingeniería Aeronáutica [hasta 2014]
Creative Commons Licenses: None

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Abstract

In this graduation thesis, a novel semi-analytical method for collision avoidance maneuver optimization is presented. It is based on a linearization of the accurate Dromo orbital elements formulation, which allows to write the optimization problem in a simple way. Predicted direct impacts have analytical solution, while non-direct impacts are reduced to a simple non-linear equation easily solvable. In this way, maximum achievable miss distance or minimum Gaussian collision probability for a fixed delta-V magnitude, or minimum delta-V magnitude for a fixed Gaussian collision probability are calculated. The accuracy of the algorithm is verified in representative mission scenarios including real cases with actual data. The method can be used for collision avoidance maneuver planning with reduced computational cost, compared to fully numerical algorithm, and is implemented in the software tool OCCAM (Optimal Computation of Collision Avoidance Maneuvers). A demo version of OCCAM is available at: http://sdg.aero.upm.es/index.php/online-apps/occam-lite

More information

Item ID: 51461
DC Identifier: http://oa.upm.es/51461/
OAI Identifier: oai:oa.upm.es:51461
Deposited by: Mr Javier Hernando Ayuso
Deposited on: 10 Jul 2018 08:27
Last Modified: 12 Jul 2018 05:14
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