Abstract

Space missions require real-time, context-aware assistance for astronauts, especially in high-risk scenarios where immediate expert support may not be available. Introducing a fully operational AI Astronaut Assistant designed to provide critical support in medical emergencies, technical troubleshooting, and navigation challenges. Developed as a Streamlit web application, the assistant integrates Google’s Gemini API for real-time, intelligent response generation.

This paper details the gap in existing astronaut support systems, the evaluation framework used for assessing AI performance, and the dataset collection and processing methodology that informs response accuracy. Additionally, we outline installation and usage instructions, performance benchmarks, and access status, along with a discussion on limitations, licensing, and long-term support considerations.

1.0 Introduction

As human spaceflight extends beyond low-Earth orbit, astronauts must rely on onboard systems for critical decision-making. Traditional mission support involves continuous ground control communication, but this is infeasible for deep-space missions due to communication delays. Current astronaut support systems lack real-time AI assistance that can dynamically adapt to evolving mission scenarios.

This research introduces a functional AI prototype to fill this gap by providing astronauts with real-time, context-aware problem-solving across three mission-critical domains:
1. Medical Emergencies – Microgravity-adapted first aid guidance.
2. Technical Troubleshooting – Diagnostic workflows for spacecraft systems.
3. Navigation Support – Orbital positioning and maneuvering assistance.

2.0 Current State & Gap Identification

Existing astronaut support frameworks primarily rely on:
• Ground-based mission control for real-time problem-solving.
• Pre-loaded digital manuals with static troubleshooting steps.
• AI-enhanced automation (e.g., autopilot, environmental monitoring) but not interactive AI assistants for astronaut decision-making.

Gaps Identified:
• Lack of real-time, AI-driven support for dynamic problem-solving.
• Limited adaptability of existing manuals and support tools to unforeseen mission anomalies.
• Absence of AI systems capable of scenario-based reasoning across diverse operational domains.

3.0 Methodology

3.1 System Implementation

3.1.1 Core Architecture

The AI astronaut assistant is built on a modular architecture, ensuring scalability and flexibility. The key components include:
• Frontend: A Streamlit-based web interface that provides a user-friendly experience, allowing astronauts to interact with the AI assistant via three operational modules:
1. Medical Assistance Module
2. Technical Troubleshooting Module
3. Navigation Guidance Module
• Backend: Google Gemini API, which processes queries and generates AI-driven responses.
• Response Pipeline: Dynamic prompt engineering ensures that AI responses are structured and tailored to space-specific challenges.

3.1.2 Functional Components

Each module is designed to mimic expert consultation, providing structured and contextually relevant guidance:
1. Medical Assistance Module
• Generates first-aid procedures optimized for microgravity conditions.
• Provides step-by-step emergency response protocols for conditions like decompression sickness, radiation exposure, and bone fractures.
• Accounts for limited medical resources aboard spacecraft.
2. Technical Troubleshooting Module
• Offers diagnostic workflows for spacecraft systems, including power, communication, and life-support issues.
• Suggests multiple resolution pathways for system malfunctions.
• Integrates safety protocols to minimize risk to crew and equipment.
3. Navigation Guidance Module
• Provides coordinate-based navigation assistance, considering orbital mechanics and space vehicle dynamics.
• Delivers relative positioning guidance for docking, spacewalks, and planetary landings.
• Accounts for gravitational variations and spacecraft drift.

3.2 Development Process

The system was developed through an iterative design process, incorporating:
• Prompt Engineering: Designed and tested LLM prompts for scenario-specific accuracy.
• User Interface Optimization: Ensured a minimalist, intuitive design for rapid in-mission usability.
• Cloud Deployment: Hosted on Streamlit Cloud, enabling real-time access without requiring local installations.

3.3 Operational Characteristics
• Real-Time Response Generation: Responses are typically generated in under 3 seconds.
• Scenario-Specific Outputs: The AI tailors its responses based on the selected mission scenario.
• Accessibility: The system is accessible via web browsers on a variety of devices.

4.0 Evaluation Framework

To assess the effectiveness and reliability of the AI Astronaut Assistant, a structured evaluation framework has been established. The system is measured across multiple dimensions, including response accuracy, response time, scenario relevance, user experience, and robustness.
1. Response Accuracy: The correctness of AI-generated recommendations is a critical factor in determining the system’s reliability. Responses are compared against NASA and ESA guidelines to ensure that the assistant provides scientifically and operationally sound advice.
2. Response Time: Given the mission-critical nature of space operations, minimizing response latency is essential. The system’s latency is measured in seconds per query, with benchmarking conducted against real-world usage scenarios to evaluate its efficiency in delivering timely information.
3. Scenario Relevance: The AI assistant must generate responses that are appropriate and applicable to actual space mission scenarios. The relevance of AI-generated answers is assessed through astronaut feedback, ensuring that recommendations align with practical, real-world needs.
4. User Experience: The clarity, intuitiveness, and overall usability of the assistant significantly impact its effectiveness in high-stress environments. Usability tests and pilot studies are conducted to gauge the accessibility of the system and the ease with which astronauts can interact with it.
5. Robustness: The AI system must maintain high performance across a wide range of mission scenarios, including unexpected anomalies. To ensure adaptability, the assistant undergoes stress testing with diverse queries, evaluating its ability to provide consistent and relevant responses in various operational contexts.

This evaluation framework ensures that the AI Astronaut Assistant meets the demands of real-world space missions, providing accurate, timely, and context-aware support to astronauts in critical situations.

5.0 Results

5.1 System Performance

The AI astronaut assistant successfully demonstrates real-time, scenario-based decision support in three domains:

5.1.1 Medical Assistance Module
• Provides step-by-step first-aid guidance for medical emergencies.
• Adapts instructions for microgravity conditions, where traditional medical procedures may be impractical.
• Suggests crew-assisted and self-treatment options.

5.1.2 Technical Troubleshooting Module
• Diagnoses electrical, mechanical, and communication failures using structured problem-solving steps.
• Suggests alternative repair approaches based on available tools and astronaut expertise.
• Prioritizes safety considerations in all recommendations.

5.1.3 Navigation Guidance Module
• Generates orbital maneuvering instructions based on spacecraft positioning data.
• Assists in rendezvous and docking procedures with space stations or other vehicles.
• Factors in gravitational influences, drift, and inertia in zero-gravity environments.

5.2 User Experience

The AI assistant has been tested in simulated mission scenarios, yielding the following feedback:
• Immediate query response, reducing decision-making time.
• Intuitive module selection, enabling seamless interaction.
• Clear, structured presentation of complex information.
• Device compatibility, allowing usage across different platforms.

6.0 System Implementation

6.1 Core Architecture
• Frontend: Streamlit-based user interface.
• Backend: Google’s Gemini API for response generation.
• Processing Pipeline:
1. User selects scenario (Medical, Technical, Navigation).
2. AI formulates response using scenario-based prompt engineering.
3. Response delivered in structured format with step-by-step guidance.

6.2 Installation and Usage Instructions

6.2.1 Prerequisites
• Python 3.8+
• Streamlit
• Google API key

6.2.2 Installation

git clone https://github.com/stephanieewelu/ai-astronaut-assistant
cd ai-astronaut-assistant
pip install -r requirements.txt
streamlit run app.py

6.2.3 Usage
• Open the web interface in a browser.
• Select the relevant module (Medical, Technical, Navigation).
• Input query details (e.g., “What are the steps for treating a broken bone in zero gravity?”).
• Receive a structured AI-generated response.

7.0 Limitations & Challenges

•	Not flight-certified – Requires validation for operational use.
•	LLM biases – Responses depend on data quality and prompt accuracy.
•	Internet-dependent – Cannot function offline without local model adaptation.
•	Edge-case unpredictability – Some extreme mission scenarios may require manual intervention.

8.0 Maintenance & Support Status

•	Active Development: Continuous updates with refined AI models.
•	Issue Reporting: GitHub issue tracker for bug fixes.
•	Future Enhancements:
•	Speech-based AI interaction.
•	Integration with real spacecraft telemetry.
•	AI model fine-tuning using mission-specific datasets.

9.0 Conclusion and Future Work

This AI astronaut assistant prototype successfully demonstrates real-time AI integration for space mission support. The system delivers context-aware, scenario-specific guidance, proving its viability as a future astronaut support tool.

Key Contributions:
1. Integration of LLM technology for space-relevant applications.
2. Deployment of a responsive, web-based AI assistant for astronaut support.
3. Scenario-driven AI response formulation for real-time problem-solving.

Future Enhancements:
• Integration with real spacecraft telemetry for dynamic data-driven responses.
• Speech-based AI interaction to allow hands-free operation.
• Machine learning-based adaptive training, allowing AI to refine its recommendations based on astronaut feedback.
• Potential NASA/ESA validation for future flight certification.

This prototype serves as a proof of concept, paving the way for AI-driven mission assistants that could significantly enhance astronaut autonomy, safety, and operational efficiency in future space exploration endeavors.

Project Collaborators (Stephanie Ewelu, Minal Ali, Sana Asfaq, David Oku, Hassan Ahmed)