Optimal Active Debris Removal mission planning to inform policy decisions

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 Δ v 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. • MIT Risk Index is used to rank the risk posed by an object around Earth orbit. • Mission planning problem is posed as a combinatorial optimization problem. • Genetic algorithm is used to search the large state space and plan the mission. • Case studies apply advanced market commitment to incentive active debris removal.