Automated multigravity assist trajectory planning with a modified ant colony algorithm

Ceriotti, M. and Vasile, M. (2010) Automated multigravity assist trajectory planning with a modified ant colony algorithm. Journal of Aerospace Computing, Information and Communication, 7(9), pp. 261-293. (doi: 10.2514/1.48448)

Abstract

The paper presents an approach to transcribe a multigravity assist trajectory design problem into an integrated planning and scheduling problem. A modified Ant Colony Optimization (ACO) algorithm is then used to generate optimal plans corresponding to optimal sequences of gravity assists and deep space maneuvers to reach a given destination. The modified Ant Colony Algorithm is based on a hybridization between standard ACO paradigms and a tabu-based heuristic. The scheduling algorithm is integrated into the trajectory model to provide a fast time-allocation of the events along the trajectory. The approach demonstrated to be very effective on a number of real trajectory design problems.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ceriotti, Dr Matteo and Vasile, Dr Massimiliano
• Authors: Ceriotti, M., and Vasile, M.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Journal of Aerospace Computing, Information and Communication
• ISSN: 1542-9423
• Copyright Holders: Copyright © 2010 American Institute of Aeronautics and Astronautics
• First Published: First published in Journal of Aerospace Computing, Information and Communication 7(9):261-293
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher