International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 11 | Nov 2025
E-ISSN: 2395-0056 P-ISSN: 2395-0072
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AI-Driven Robotics in Space Exploration AVINASH K S MSc Computer Science Student, St. Thomas (Autonomous) College, Thrissur 680001, Kerala, India
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Artificial Intelligence (AI) and robotics are transforming the landscape of space exploration by enabling
autonomous decision-making, adaptive mission planning, and resilient operations in extreme environments. This paper presents a consolidated review of recent advancements in AI-driven robotics, focusing on applications in optical experimentation, planetary surface exploration, spacecraft guidance, and mission optimization. The discussion integrates developments from five key research works: the OptoMate platform for automating free-space optics experiments using finetuned large language models (LLMs) and precision robotics; JAXA’s deployment of autonomous rovers and pinpoint landing systems in asteroid and lunar missions; legal and governance analyses addressing the risks and liabilities of AI operations in space; deep reinforcement learning (DRL) frameworks for navigation, hazard detection, and resource allocation; and evolutionary optimization techniques for interplanetary trajectory design. The review identifies performance gains achieved in both simulated and real- world missions while highlighting limitations in explainability, simulation-to-reality transfer, and regulatory compliance. Future research directions include hybrid AI-human decision systems, legal-aware mission planning, and explainable AI for high- stakes operations. This synthesis aims to guide researchers toward more reliable, transparent, and accountable AI- driven space systems. Key Words: Artificial Intelligence (AI); Space Robotics; Autonomous Systems; Deep Reinforcement Learning (DRL); Planetary Exploration; Optical Experimentation; Large Language Models (LLMs); Evolutionary Optimization; Explainable AI (XAI); Spacecraft Guidance; Trajectory Design; AI Governance; Adaptive Mission Planning.
I.INTRODUCTION Space exploration has come a long way— from the early days of fully human- controlled missions to today’s use of smart, independent systems. Artificial Intelligence (AI) and robotics are now at the center of this change. They help spacecraft and rovers work on their own, make quick decisions, and adapt to unexpected problems. This is especially important for missions far from Earth, where communication delays can take several minutes and Conditions can change without warning. With AI, robots in space can plan their paths, avoid dangers, use resources wisely, and complete their goals without constant human instructions. In recent years, technologies like deep reinforcement learning (DRL), large language models (LLMs), computer vision, and evolutionary optimization have greatly improved what autonomous systems can do. For example, AI-powered robots can now design and set up complex optical experiments in a lab. DRL-based systems can help rovers find safer and faster routes on difficult terrain. Algorithms inspired by nature can also guide spacecraft along the best possible path while balancing fuel, time, and safety. Space agencies like Japan’s JAXA, Europe’s ESA, and the United States’ NASA have already shown how powerful these technologies can be. JAXA’s Hayabusa-2 mission used small hopping rovers to explore an asteroid’s surface without human control, and their SLIM lander demonstrated highly accurate landing technology. ESA and NASA are also testing AI for coordinating groups of satellites, servicing spacecraft in orbit, and navigating in dangerous planetary environments. These advances make missions more efficient and open the door for long-term projects like building a base on the Moon or exploring Mars. But as AI becomes more common in space missions, it brings new challenges. The rules and laws for space activities were written decades ago and don’t fully cover AI’s unique issues, like who is responsible if an autonomous system causes an accident, or how to handle massive amounts of space-generated data. This means engineers, scientists, and policy-makers need to work together to make sure AI systems are safe, trustworthy, and follow international space laws. This paper reviews recent progress in AI- based robotics for space missions. It looks at five key areas: AI in optical experiments, robotics for planetary exploration, legal and policy issues, DRL-based mission planning, and advanced algorithms for spacecraft guidance. By studying these areas, we highlight the main achievements, the challenges that remain, and possible future directions to make AI systems for space more reliable, transparent, and ready for real missions.
I.
Literature Review
Uddin et al. (2025) presented the OptoMate platform, a groundbreaking example of combining generative AI with robotics for the automation of free-space optical experiments. The system integrates a fine- tuned LLaMA3.1-8B-Instruct large language model with Quantum-informed Tensor Adaptation (QuanTA) to design optical setups that are both spatially and
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