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Optimal Active Debris Removal mission planning to inform policy decisions
Acta Astronautica ( IF 3.1 ) Pub Date : 2024-12-02 , DOI: 10.1016/j.actaastro.2024.11.050
Nihal Simha, Simone Servadio, Miles Lifson, Giovanni Lavezzi, Richard Linares

Active Debris Removal (ADR) will be important to ensure the long-term sustainability of the space environment and to preserve benefits derived from space for those here on Earth. Effective ADR relies on optimal mission planning to select Space Objects (SOs) with the highest risk. The Advanced Market Commitment (AMC) concept is discussed as a method to increase the quantity of ADR over market equilibrium to promote space sustainability and accelerate the development of commercial ADR. The MIT Orbital Capacity Assessment Tool-Monte Carlo (MOCAT-MC) is utilized to obtain information about the probability of collision with other SOs and the number of debris generated over a long propagation and is combined with the criticality of spacecraft index, a static ranking index, to obtain the MIT Risk Index (MITRI), which quantifies the risk posed by an SO. A Genetic Algorithm (GA) is used with a binary chromosome encoding to perform evolutionary optimization and identify the optimal mission route by solving a mixed integer nonlinear problem while respecting spacecraft Δv and thrust constraints. MITRI is demonstrated as an efficacy metric to inform subsidy pricing for an AMC. Three potential AMC case studies are described: a government customer selecting objects for removal, a servicer selecting and optimizing an object set based on total environmental risk and subsidy, and a commercial operator paying for optimized remediation in their particular region of space. MITRI provides a mechanism to align private and public incentives for risk reduction.

中文翻译:


最佳主动碎片清除任务规划,为政策决策提供信息



主动碎片清除 (ADR) 对于确保太空环境的长期可持续性以及为地球上的人们保留太空带来的好处非常重要。有效的 ADR 依赖于最佳任务规划来选择风险最高的空间对象 (SO)。讨论了高级市场承诺 (AMC) 概念作为一种在市场均衡的情况下增加 ADR 数量的方法,以促进空间可持续性并加速商业 ADR 的发展。麻省理工学院轨道容量评估工具-蒙特卡洛 (MOCAT-MC) 用于获取有关与其他 SO 碰撞的概率和在长距离传播中产生的碎片数量的信息,并与航天器临界度指数(静态排名指数)相结合,获得麻省理工学院风险指数 (MITRI),该指数量化了 SO 带来的风险。遗传算法 (GA) 与二进制染色体编码一起使用,通过求解混合整数非线性问题来执行进化优化并确定最佳任务路线,同时考虑航天器 Δv 和推力约束。MITRI 被证明是告知 AMC 补贴定价的有效性指标。描述了三个潜在的 AMC 案例研究:政府客户选择要移除的对象,服务商根据总体环境风险和补贴选择和优化对象集,以及商业运营商为其特定空间区域的优化修复付费。MITRI 提供了一种机制,使私人和公共激励措施保持一致以降低风险。
更新日期:2024-12-02
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