Multi-Target Spacecraft Mission Design using Convex Optimization and Binary Integer Programming
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916578913681408 |
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| author | Yarndley, Jack Holt, Harry Armellin, Roberto |
| author_facet | Yarndley, Jack Holt, Harry Armellin, Roberto |
| contents | The optimal design of multi-target rendezvous and flyby missions is challenging due to the combination of traditional spacecraft trajectory optimization and high-dimensional combinatorial problems. This often requires large-scale global search techniques or simplified approximations that rely on manual tuning to be performant. While global search techniques are typically computationally expensive, limiting their use in time- or cost-constrained scenarios, this work proposes a computationally efficient nested-loop approach. The problem is split into separate combinatorial and optimal control subproblems: the combinatorial problem is solved using Binary Integer Programming (BIP) with a fixed rendezvous time schedule, while the optimal control problem is handled with adaptive-mesh Sequential Convex Programming (SCP), which also optimizes the time schedule. By iterating these processes in a nested-loop structure, the approach can efficiently find high-quality solutions. When, applied to the Global Trajectory Optimization Competition 12 (GTOC 12) problem, this method results in several new best-known solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Target Spacecraft Mission Design using Convex Optimization and Binary Integer Programming Yarndley, Jack Holt, Harry Armellin, Roberto Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics The optimal design of multi-target rendezvous and flyby missions is challenging due to the combination of traditional spacecraft trajectory optimization and high-dimensional combinatorial problems. This often requires large-scale global search techniques or simplified approximations that rely on manual tuning to be performant. While global search techniques are typically computationally expensive, limiting their use in time- or cost-constrained scenarios, this work proposes a computationally efficient nested-loop approach. The problem is split into separate combinatorial and optimal control subproblems: the combinatorial problem is solved using Binary Integer Programming (BIP) with a fixed rendezvous time schedule, while the optimal control problem is handled with adaptive-mesh Sequential Convex Programming (SCP), which also optimizes the time schedule. By iterating these processes in a nested-loop structure, the approach can efficiently find high-quality solutions. When, applied to the Global Trajectory Optimization Competition 12 (GTOC 12) problem, this method results in several new best-known solutions. |
| title | Multi-Target Spacecraft Mission Design using Convex Optimization and Binary Integer Programming |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2411.11281 |