Enhanced Autonomy for Next-Generation Rover Missions using Artificial Intelligence and Machine Learning (AI/ML)
- Paper number
GLEX-2025,14,1,6,x92644
- Author
Ms. Chris Gurjao, Astronautical Society of India, India
- Coauthor
Mr. Vicksan Gurjao, India
- Year
2025
- Abstract
Artificial intelligence (AI) and Machine Learning (ML) are two Computer Science fields currently undergoing rapid changes that may revolutionize the future of space exploration. This paper proposes a paradigm for the development and integration of AI-enhanced autonomy in the next generation of rovers. Such autonomy would decrease the reliance of rovers on Earth-based control and also optimize the real-time decision-making capabilities of the rovers. The research carried out focuses on how AI can increase the autonomy of extraterrestrial missions, thereby enabling a more efficient exploration of celestial bodies by leveraging satellite communication for data exchange and collaboration with Earth-based systems. The integration of robust AI techniques into various hardware and sensor systems can enable rovers to navigate across rough terrain, detect and avoid obstacles in their path, and adjust to changing environmental conditions without requiring immediate human assistance. Path-planning algorithms and ML techniques allow rovers to explore environments that are dangerous for humans, thereby helping researchers back on Earth gain additional knowledge about extraterrestrial geology and climate. AI/ML can therefore assist rovers to operate more effectively in the harsh and unpredictable environments of extraterrestrial bodies by improving the autonomy of rovers in navigation, obstacle avoidance, resource management, scientific analysis, time communication delays, and energy management. AI-driven rovers not only have the potential to enhance mission success, but also to advance our understanding of space and pave the way for future telerobotics missions. Another benefit of using AI/ML in rovers is real-time regolith collection and analysis. ML algorithms can identify and analyse rock and soil samples gathered by the rover, enabling the immediate interpretation of the data collected. This increases the mission progress speed and also decreases the energy required for the continuous transmission of rover data to Earth, an essential requirement because of the distance and communication delay involved in extraterrestrial missions. In addition, AI-driven sample analysis can support In-Situ Resource Utilization (ISRU) and thereby contribute to future manned space missions. Autonomous rovers using AI/ML therefore have broader implications for space missions such as the potential to improve human-machine collaboration in future telerobotics missions, solve important operations challenges like delayed communication with the Earth, and prepare extraterrestrial locations for future manned missions. Keywords: Artificial intelligence (AI), Machine Learning (ML), autonomy, rovers, extraterrestrial missions, telerobotics
- Abstract document
- Manuscript document
GLEX-2025,14,1,6,x92644.pdf (🔒 authorized access only).
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