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LLM-driven agent HELIOS automates spacecraft trajectory optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HELIOS, an autonomous agent that uses a large language model (LLM) to optimize spacecraft trajectories for deep-space missions. This system tackles key challenges in indirect trajectory optimization, such as deriving complex conditions and adapting to different dynamics models. HELIOS can autonomously derive symbolic conditions, verify them with SymPy, generate C++ code for numerical solutions, and solve problems ranging from simple rendezvous to complex multi-leg transfers and solar-sail trajectories. Experiments show a high success rate, with larger LLMs demonstrating better derivation capabilities. AI

IMPACT Automates complex scientific calculations, potentially accelerating deep-space mission design and research.

RANK_REASON Academic paper detailing a new LLM-driven agent for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-driven agent HELIOS automates spacecraft trajectory optimization

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Academic paper detailing a new LLM-driven agent for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · An-yi Huang ·

    HELIOS: An LLM-Driven Autonomous Indirect Trajectory Optimization Agent

    arXiv:2607.24051v1 Announce Type: cross Abstract: Low-thrust trajectory optimization is a core technology in deep-space mission design. Indirect methods based on Pontryagin's Minimum Principle (PMP) offer rigorous optimality guarantees, yet their practical application faces three…

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