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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. ISSN 1542-9423

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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 manoeuvers 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.