Active Environmental Monitoring and Anomaly Search System for Space Habitat With Markov Decision Process and Active Sensing

Yanjie Guo,Zhaoyi Xu,Joseph Homer Saleh

doi:10.1109/access.2021.3068950

Abstract

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 Markov decision process 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 10~15 times better performance than the traditional fixed sensor monitoring and detection strategy; ventilation in the monitored environment affects the performance by 6~40 times for any monitoring architecture with fixed or moving sensors; the monitoring performance cannot be fully reflected in a monolithic, single metric, but should include different metrics for the timeliness and spatial resolution of the detection function.

Highlights

The Environmental Control and Life Support System (ECLSS) is a core element for human space exploration missions
We focus on improving the performance of the environmental monitoring and anomaly detection systems to address more stringent requirements for future space exploration missions
We have developed a decentralized policymaker for a multi-agent system using our dynamic value iteration (DVI) method based on the reward function expressed in Eq 10

Summary

Introduction

The Environmental Control and Life Support System (ECLSS) is a core element for human space exploration missions. It consists of subsystems that provide and control necessary elements for human survival, including atmosphere monitoring and revitalization, fire detection and suppression, water recycle and recovery, and waste management. For future missions that could last years with limited ground support, the ECLSS will likely further evolve to meet new, more stringent reliability and autonomy requirements [4]. The associate editor coordinating the review of this manuscript and approving it for publication was Guoguang Wen. cause in a future deep space habitat [5], it is essential to have a smart environmental monitoring system for the ECLSS that can search and detect anomalies autonomously and in a timely manner [6]. This work addresses in part these issues by developing an active monitoring and anomaly search system using Markov decision process (MDP) and moving sensors

Objectives

Results

Conclusion