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 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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 261-293 |
| Number of pages | 32 |
| Journal | Journal of Aerospace Computing, Information, and Communication |
| Volume | 7 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 30 Sept 2010 |
Keywords
- multigravity assist trajectory design
- ant colony optimization algorithm
- planning