Intelligent Health Management for an Autonomous Lunar Rover

The increasing demand for autonomous robotic systems in extended space exploration missions necessitates robust health management frameworks to ensure operational reliability in challenging and extreme extraterrestrial environments. This paper proposes an Integrated Vehicle Health Management (IVHM) framework designed to support NASA's RESOURCE Lunar Mission, with a focus on enabling the sustained operation of an autonomous lunar polar rover tasked with exploiting ice deposits at the lunar poles. The framework employs artificial intelligence in fault detection and diagnostic reasoning modules, using real-time sensor data to identify, predict, and mitigate subsystem faults. This approach enhances system reliability, safety, and mission success rates while reducing operational costs. Emphasis is placed on mission-critical communication and navigation subsystems, evaluated through digital twin simulations and structured fault injection techniques under nominal and degraded conditions. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed as the IVHM reasoning engine to deliver interpretable fault diagnosis, early degradation detection, and rapid subsystem reconfiguration in dynamic operational scenarios. The results demonstrate the feasibility and efficacy of the proposed IVHM framework in advancing autonomous fault management for planetary exploration vehicles, providing a pathway for safer, more resilient, and cost-effective space missions.