Convex Maneuver Planning for Spacecraft Collision Avoidance

TL;DR

This paper introduces a convex optimization-based algorithm for designing low-thrust collision avoidance maneuvers for spacecraft, enabling efficient and globally optimal solutions for short-term conjunctions in dense satellite environments.

Contribution

It formulates the maneuver planning as a nonconvex QCQP and relaxes it to a convex SDP, ensuring globally optimal solutions and handling collision probability constraints effectively.

Findings

The relaxation is empirically tight, allowing recovery of global optima.

The algorithm produces minimum-energy, minimum-risk collision avoidance maneuvers.

Validation shows effective collision risk reduction in high-fidelity simulations.

Abstract

Conjunction analysis and maneuver planning for spacecraft collision avoidance remains a manual and time-consuming process, typically involving repeated forward simulations of hand-designed maneuvers. With the growing density of satellites in low-Earth orbit (LEO), autonomy is becoming essential for efficiently evaluating and mitigating collisions. In this work, we present an algorithm to design low-thrust collision-avoidance maneuvers for short-term conjunction events. We first formulate the problem as a nonconvex quadratically-constrained quadratic program (QCQP), which we then relax into a convex semidefinite program (SDP) using Shor's relaxation. We demonstrate empirically that the relaxation is tight, which enables the recovery of globally optimal solutions to the original nonconvex problem. Our formulation produces a minimum-energy solution while ensuring a desired probability of collision at the time of closest approach. Finally, if the desired probability of collision cannot be satisfied, we relax this constraint into a penalty, yielding a minimum-risk solution. We validate our algorithm with a high-fidelity simulation of a satellite conjunction in low-Earth orbit with a simulated conjunction data message (CDM), demonstrating its effectiveness in reducing collision risk.