Active Sensing for Space Habitat Environmental Monitoring and Anomaly Detection

For future crewed missions that could last years with limited ground support, the environmental control and life support system (ECLSS) will likely evolve to meet new, more stringent reliability and autonomy requirements. In this work, we focus on improving the performance of the environmental monitoring and anomaly detection systems using reinforcement learning and active sensing. We exploit actively moving sensors to develop a novel sensing architecture and supporting analytics, termed Active environmental Monitoring and Anomaly Search System (AMASS). We design a Dynamic Value Iteration policy to solve the path planning problem for the moving sensors in a dynamic environment. To test and validate AMASS, we developed a series of computational experiments for fire search, and we assessed the performance against three metrics: (1) anomaly detection time lag, (2) source location uncertainty, and (3) state estimation error. The results demonstrate that AMASS provides significant advantages over the traditional fixed sensor monitoring and detection strategy. In future work, we propose to add ventilation in the monitored environment and examine its effect on the performance of both AMASS and the traditional monitoring strategy with fixed sensors.