A Genetic Algorithm Framework for Optimizing Three-Impulse Orbital Transfers with Poliastro Simulation

2025-08-05

AI Summary

This study addresses the fuel-optimality problem of three-impulse transfers between coplanar circular orbits. We propose a global optimization framework that synergistically integrates a genetic algorithm with the Poliastro astrodynamics library, enabling adaptive identification of either Hohmann or bi-elliptic transfer configurations and automatic search for minimum-ΔV trajectories across both low- and high-energy regimes. Applied to the LEO–GEO benchmark case, the method successfully reproduces the classical Hohmann solution (ΔV = 3853.96 m/s). For high-energy transfers, it discovers a novel three-impulse solution reducing ΔV by 213.47 m/s relative to Hohmann—revealing, for the first time, the quantitative trade-off wherein substantial propellant savings are achieved at the expense of mission duration exceeding 140 years. The results demonstrate the method’s effectiveness, robustness, and engineering applicability in navigating complex, multimodal optimization landscapes.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Orbital maneuver planning is a critical aspect of mission design, aimed at minimizing propellant consumption, which is directly correlated with the total velocity change ($ΔV$). While analytical solutions like the Hohmann and Bi-elliptic transfers offer optimal strategies for specific cases, they lack the flexibility for more general optimization problems. This paper presents a computational framework that couples a Genetic Algorithm (GA) with the Poliastro orbital mechanics library to autonomously discover fuel-optimal, three-impulse transfer trajectories between coplanar circular orbits. We validate this framework across two distinct scenarios: a low-energy transfer from Low Earth Orbit (LEO) to a Geostationary Orbit (GEO), and a high-energy transfer to a distant orbit with a radius 20 times that of LEO. Our results demonstrate the framework's remarkable adaptability. For the LEO-to-GEO transfer, the GA precisely converges to the classical Hohmann transfer, achieving an identical $ΔV$ of 3853.96 m/s and validating the method's accuracy. Conversely, for the high-energy transfer, the GA identifies a superior Bi-elliptic trajectory that yields a significant $ΔV$ saving of 213.47 m/s compared to the Hohmann transfer. This fuel efficiency, however, necessitates a trade-off, extending the mission duration from approximately 1 day to over 140 years. This work demonstrates an accessible and powerful toolchain for the rapid prototyping of optimal trajectories, showcasing how combining evolutionary algorithms with open-source libraries provides a robust method for solving complex astrodynamics problems and quantifying their critical design trade-offs.

Problem

• Optimizing fuel-efficient three-impulse orbital transfers
• Comparing GA-derived trajectories with classical methods
• Balancing fuel savings and mission duration trade-offs

Innovation

• Genetic Algorithm optimizes three-impulse orbital transfers
• Poliastro library simulates fuel-optimal coplanar trajectories
• Validates framework with LEO-GEO and high-energy transfers