IEEE Robotics & Automation Magazine - March 2012 - 36
faster than the maximum robot motion capabilities, as well
as tracking processes with splitting boundaries (e.g.,
[111]). These processes are of particular interest to atmospheric and oceanographic scientists. Other deficient areas
include methods of adaptive sampling to minimize uncertainty in data-assimilated models, as well as real-time,
onboard image processing strategies to classify and track
various items of interest.
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.
References
Cooperative Systems
To address many of the proposed larger environmental
monitoring problems, frameworks for integration of
disparate sensing platforms, as well as for robot and sensor network interaction and information sharing are necessary. Methods are required for resource allocation to
solve various observation objectives, as well as decentralized cooperative control of large groups of mobile sensing
systems, particularly with low-communication bandwidth and significant asynchronicities and latencies in
data transmission and information processing, and GPSdenied environments.
Classification and Information Extraction
As discussed in the "Natural Habitat Mapping" section,
robotic systems are now capable of generating enormous
volumes of data. This data can be relatively easily transformed into spatial maps. However, the extraction of
information for generation of data products to be used by
managers and scientists is still predominantly performed
manually. There will become a point where complete
manual processing is unsustainable. Therefore, automated techniques, such as those employing machine
learning (see the "Natural Habitat Mapping" section), are
required to preclassify data. However, reliable segmentation and classification in naturally lit and varying scale
scenes, as well as the ability to resurvey and detect and
quantify change in dynamic environments remain significant challenges.
Conclusions
This article summarizes two decades of literature relating
to robotics for environmental monitoring focusing on key
research activities and applications, their operating
domains, and their real-world validation. 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 (e.g.,
UAVs), 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
36
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IEEE ROBOTICS & AUTOMATION MAGAZINE
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MARCH 2012
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