Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output. During the 11th edition of the GTOC challenge, participants were tasked with a complex trajectory planning related to the construction of a precursor Dyson structure, a heliocentric ring made of twelve stations. To this purpose, we developed several new approaches that synthesize techniques from machine learning, combinatorial optimization, planning and scheduling, and evolutionary optimization effectively integrated into a fully automated pipeline. These include a machine learned transfer time estimator, improving the established Edelbaum approximation and thus better informing a Lazy Race Tree Search to identify and collect asteroids with high arrival mass for the stations; a series of optimally-phased low-thrust transfers to all stations computed by indirect optimization techniques, exploiting the synodic periodicity of the system; and a modified Hungarian scheduling algorithm, which utilizes evolutionary techniques to arrange a mass-balanced arrival schedule out of all transfer possibilities. We describe the steps of our pipeline in detail with a special focus on how our approaches mutually benefit from each other. Lastly, we outline and analyze the final solution of our team, ACT&Friends, which ranked second at the GTOC 11 challenge.
翻译:Dyson球场是假设的巨型结构,环绕恒星,以获取大部分能量产出。在GTOC挑战第11版期间,参与者承担了与建造由12个站站组成的热心环组成的先质Dyson结构有关的复杂轨迹规划任务。为此,我们开发了一些新方法,将机器学习、组合优化、规划和时间安排以及进化优化等技术综合起来,有效地纳入一个完全自动化的管道。其中包括一个机器学习的传输时间估计器,改进已经建立的Edelbaum近距离,从而更好地通报Lazy 种族树搜索,以查明和收集到达地质量高的小行星;一系列以间接优化技术计算到所有站点的最优化阶段的低入侵性转移;以及一个匈牙利经修改的排程算法,利用演进技术安排一个从所有传输可能性中得出的大规模平衡的抵达时间表。我们详细描述了我们的管道的步骤,特别侧重于我们的方法如何相互受益。最后的解决方案在11和GTOC中排名。