Robotics for Environmental Monitoring

Robotic systems are increasingly being utilized as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today’s robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences. The need for large-scale persistent environmental monitoring has become particularly relevant in recent times after a set of serious natural disasters and environmentally harmful accidents. These include earthquakes, tsunamis, hurricanes, floods, large forest fires, volcanic eruptions, oil spills, and nuclear meltdowns

However, understanding and quantifying environmental health, processes, responses to stressors, and trajectories require large amounts of accurate spatial and temporally disperse data. For example, meteorologists need to monitor a set of physical variables such as temperatures, airflow, and air pressure, to study the weather and to forecast its behavior. Environmental scientists study the transport and dispersion of air or water pollution. The monitoring of both physical and chemical quantities, such as airflow, and polluting gases such as CO2, CO, CO2, or NOx, for example, is important to model and track the involved phenomena. Ecologists study the systems that may involve the monitoring of the previous physical and chemical quantities along with the detection, classification, and tracking of living organisms in their environments.

To meet these data requirements, at a global scale, remote-sensing satellites are typically utilized, while at the regional scale, fixed monitoring stations are mainly employed. At the local scale, manual and automated sampling is typically conducted. This can be an extremely difficult task and often limits data collection, particularly, during significant events such as hurricanes and floods. Furthermore, local scale sampling is often costly and difficult to maintain persistent sampling. However, over the past few decades, sensor networks have emerged as a new tool to collect spatially dense information in real time from natural environments. Although there is a progress compared with past methodologies, traditional sensor networks only provide fixedmonitoring points without themeans to adapt to changes in the surrounding environment. To increase data collection efficiencies, particularly in hostile environments, earth and life scientists see robotics as a promising tool with the capacity to improve their current means to observe and collect data about natural processes or phenomena at vast spatial and temporal scales. Oceanographers were among the first earth scientists who started using underwater robots to study the deep ocean and seafloor [1]. Geologists have also explored the use of robots to study extreme phenomena such as vulcanology [2], while some meteorologists have began using robotic aircraft in the study ofmicroclimates and hurricane observation [3]. Robotics science has made huge progresses since the arrival of the first commercial robots on the factory floor more than 50 years ago. Principally, robots have received new and better sensors, along with algorithms that provide the means to perceive their operating environment and plan missions autonomously while reacting to various uncertainties. Nowadays, robots can be seen operating in natural or in man-made, highly unstructured environments, such as deep oceans [4], active volcanoes [5] (Figures 1 and 2), or damaged nuclear power plants [6]. Although a large range of fundamental problems still need to be solved, operating in such hostile and challenging environments has established a new frontier for robotics as well as environmental sciences.