AI 中文总结
GTOC13提出系外行星太阳帆航天器轨迹设计问题，“对映点”团队的解决方法结合贸易研究、波束搜索、共振目标策略和顺序凸规划，通过无损控制凸太阳帆公式优化轨迹，最终获得第三名，证明了相关方法的可扩展性。
AI 中文摘要
太阳帆为大规模、长时间轨迹设计问题提供了一种有吸引力但具有挑战性的推进方法。2025年，第13届全球轨迹优化竞赛（GTOC13）提出了一个轨迹设计问题，涉及虚构的阿尔泰拉系统中的系外行星太阳帆航天器，目标是从行星、彗星和小行星的飞越中获取科学回报。高分解决方案将组合重力辅助旅行设计与连续太阳帆轨迹优化相结合。本文介绍了“对映点”团队在GTOC13期间开发的解决方案。该方法结合了几个搜索和优化阶段：贸易研究以识别有竞争力的进入机会；对弹道重力辅助旅行进行大规模波束搜索以识别有益的行星结构；对祝融星飞越序列采用共振目标策略；使用顺序凸规划（SCP）对多段太阳帆轨迹进行细化。细化过程的一个关键组成部分是使用无损控制凸太阳帆公式，该公式允许同时优化轨迹的大部分，包括所有重力辅助几何形状和飞越时间，以最大化分数。最终轨迹获得第三名，133次计分飞越的得分为337.878，其结构与其他高分解决方案大致相似。这证明了SCP等方法在解决非常大的轨迹设计问题时的可扩展性。
英文摘要
Solar sails present an attractive but challenging propulsion method for large-scale, long-duration trajectory design problems. In 2025, the 13th Global Trajectory Optimization Competition (GTOC13) presented a trajectory design problem involving an exoplanetary solar sailing spacecraft in the fictional Altaira system, where the goal is to collect scientific return from flybys of planets, comets, and asteroids. High-scoring solutions combine combinatorial gravity assist tour design with continuous solar sail trajectory optimization. This paper presents the solution approach developed by the team `TheAntipodes' during GTOC13. The approach combines several search and optimization stages: (1) trade studies to identify competitive entry opportunities, (2) large-scale beam search over ballistic gravity assist tours to identify beneficial planetary structures, (3) resonant targeting strategies for Vulcan flyby sequences, and (4) multi-leg solar sail trajectory refinement using sequential convex programming (SCP). A key component of the refinement process is the use of a lossless control-convex solar sail formulation, which allows for large portions of the trajectory, including all gravity assist geometry and flyby timing, to be optimized simultaneously to maximize score. The resulting trajectory placed third, with a score of 337.878 from 133 scoring flybys, and exhibited a structure broadly similar to those of the other high-scoring solutions. This demonstrates the scalability of methods such as SCP for very large trajectory design problems.
