Robotics for Environmental Monitoring

By Matthew Dunbabin and Lino Marques

NOTE: This is an overview of the entire article, which appeared in the March 2012 issue of the IEEE Robotics & Automation magazine.
Click here to read the entire article.

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. However, understanding and quantifying environmental health, processes, responses to stressors, and trajectories require large amounts of accurate spatial and temporally disperse data. 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. But fixed stations can not adapt to changes in the environment. At the local scale, manual and automated sampling is typically conducted. But this can be difficult, especially during significant weather events.

Robotics have allowed earth and life scientists to improve their current means to observe and collect data about natural processes or phenomena at vast spatial and temporal scales. while reacting to various uncertainties. Nowadays, robots can be seen operating in natural or in man-made, highly unstructured environments, such as deep oceans, active volcanoes (Figure 1), or damaged nuclear power plants. 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.

Figure 1. Robovolc operating on Mt. Etna, Italy, Europe's largest active volcano. (Photo courtesy of University of Catania.)

Figure 1. Robovolc operating on Mt. Etna, Italy, Europe’s largest active volcano. (Photo courtesy of University of Catania.)

The article provides extensive background information and descriptions of sensors, sensor networks, and sampling platforms in use today in the field. Applications such as mapping and detecting changes in habitats, detection of marine and atmospheric plumes, and ‘data muling’ (collecting data rom fixed sensor nodes using mobile robots) are explained. A key advantage of robotics in applications like this is the ability to adapt the data collection to changing conditions in the environment being monitored. The efficiency this permits would not be possible without robotics.

A significant proportion of research focus has been on marine-based robotic systems. Hence, these are the most mature in terms of vehicle design, endurance, and scientific application base. However, in recent years, as the reliability of research and commercially available systems has improved, other application domains have emerged, particularly atmospheric observation. This has encouraged new trends in environmental robotics science relating to robot and sensor network interaction, model-aided path planning, adaptive sampling, and cooperative robotic teams. However, some significant research challenges remain to be solved before these systems become ubiquitous scientific tools. These include vehicle control, reliability and safety, real-time dynamic process tracking, mission and task planning, and managing large cooperative robot teams. Addressing these research challenges over the coming years will see robotic systems play an increasing role in scientific data collection, advancing our fundamental knowledge of the environment and its processes.


Matthew Dunbabin, Autonomous Systems Laboratory, CSIRO ICT Centre, Kenmore (QLD), Australia. E-mail:

Lino Marques, Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, Portugal. E-mail: