TY - JOUR

T1 - A survey on autonomous environmental monitoring approaches

T2 - towards unifying active sensing and reinforcement learning

AU - Mansfield, Dave

AU - Montazeri, Allahyar

PY - 2024/3/12

Y1 - 2024/3/12

N2 - The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment’s changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

AB - The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment’s changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

KW - Environmnetal Monitoring

KW - Active SLAM

KW - Reinforcement learning

KW - Autonomous Robots

KW - Multiagent reinforcement learning

U2 - 10.3389/frobt.2024.1336612

DO - 10.3389/frobt.2024.1336612

M3 - Review article

C2 - 38533524

VL - 11

JO - Frontiers in Robotics and AI

JF - Frontiers in Robotics and AI

SN - 2296-9144

M1 - 1336612

ER -