Fuel-Optimal Collision Avoidance Maneuvers in Long-Term Encounters with Station-Keeping Constraints

TL;DR

This paper introduces a sequential convex programming method for calculating fuel-efficient collision avoidance maneuvers over long-term encounters, incorporating station-keeping constraints and probabilistic safety metrics.

Contribution

It develops a novel approach combining long-term collision probability metrics with station-keeping constraints, suitable for autonomous satellite operations.

Findings

Method yields fuel-efficient collision avoidance maneuvers.

Approach is applicable to different orbital regimes.

Numerical simulations demonstrate practical run-time performance.

Abstract

This work presents a sequential convex program method to compute fuel-optimal collision avoidance maneuvers for long-term encounters. The low-thrust acceleration model is used to account for the control, but the method can compute high-thrust maneuvers by increasing the maximum available acceleration. Dealing with the long-term conjunction poses additional challenges compared to the short-term problem because the encounter is not instantaneous. Thus, under the assumption of Gaussian statistics, the probability of collision is replaced by a simpler metric, the instantaneous probability of collision (IPoC) and a keep-out zone constraint is formulated as a continuous condition to be respected throughout the time frame of interest. The robustness of the solution is improved by introducing a constraint on the sensitivity of the IPoC. Furthermore, the collision avoidance problem is coupled with the classical station-keeping requirement for geostationary Earth orbit satellites and with a return to the nominal orbit condition for low Earth orbit satellites. Even though no guarantee is given for the recovery of the global optimum solution, numerical simulations in different orbital regimes show that the proposed approach can yield a local fuel-optimal solution with a run-time suitable for autonomous applications.