Orbital Depot Location Optimization for Satellite Constellation Servicing with Low-Thrust Transfers

TL;DR

This paper presents a novel method for optimizing orbital depot locations in continuous space for satellite servicing, combining mixed-integer and nonlinear programming, demonstrated on a GPS constellation case study.

Contribution

It introduces a continuous-space depot location optimization approach using a decoupled iterative solution methodology, advancing beyond traditional fixed-node network models.

Findings

Method effectively optimizes depot locations in continuous space.

Numerical experiments show stable and promising solutions.

Applicable to real-world satellite constellation servicing scenarios.

Abstract

This paper addresses the critical problem of co-optimizing the optimal locations for orbital depots and the sequence of in-space servicing for a satellite constellation. While most traditional studies used network optimization for this problem, assuming a fixed set of discretized nodes in the network (i.e., a limited number of depot location candidates), this work is unique in that it develops a method to optimize the depot location in continuous space. The problem is formulated as mixed-integer nonlinear programming, and we propose a solution methodology that iteratively solves two decoupled problems: one using mixed-integer linear programming and the other using nonlinear programming with an analytic transfer solution. To demonstrate the effectiveness of our approach, we apply this methodology to a case study involving a GPS satellite constellation. Numerical experiments confirm the stability of our proposed solutions.