Task Allocation and Trajectory Optimization for Mother-Daughter Satellite Clusters Observing Non-Cooperative Constellations via Improved Genetic Algorithm

Abstract

Over the past decade, the rapid deployment of Low Earth Orbit (LEO) mega-constellations has significantly reshaped the spatial distribution of the near-Earth environment and increased orbital density to historically high levels. While popularizing space access, the accumulation of defunct or non-cooperative satellites raises severe concerns regarding space sustainability and the Kessler syndrome. Consequently, the ability to conduct active, multi-target close-proximity inspection is becoming an essential capability for Space Situational Awareness (SSA). Traditional monolithic servicing strategies rely on sequential point-to-point orbital transfers, which have to face heavy computation cost and restrictive fuel constraints when targeting multi-planar distributed networks requiring continuous out-of-plane maneuvers and phasing adjustments. In order to overcome these limitations, the paper proposes a coordinated mother-daughter satellite cluster architecture. A primary mother satellite serves as a deployment hub, while a swarm of highly maneuverable daughter satellites executes sequential "Observe-and-Return" missions, gathering high-fidelity SSA data. Assigning these multi-target inspection missions to a limited fleet creates an NP-hard combinatorial challenge, further compounded by strictly non-overlapping operational time windows and rigid propellant boundaries. To navigate this high-dimensional search space, the study introduces an improved Genetic Algorithm (GA) featuring a dynamic chronological decoding mechanism. Furthermore, to eliminate secular positional drifts during orbital integration, a high-fidelity five-impulse maneuver sequence is established, embedding an autonomous Lambert-based positional compensation mechanism. Simulation results indicate that the proposed method yields scalable, fuel-feasible, and load-balanced kinematic baselines for autonomous target characterization across large-scale constellations. Additionally, an event-triggered dynamic re-planning mechanism is utilized to robustly adapt to uncooperative evasive maneuvers in highly dynamic real-world space scenarios.