Cooperative V
441
Part B | 19.1
19. Cooperative Vehicle Environmental Monitoring
Naomi Ehrich Leonard
This chapter reviews cooperative control of autonomous vehicles for environmental monitoring
with a focus on methodologies that have been
designed, deployed, and proven to provide efficient, reliable, and sustained monitoring of the
uncertain and inhospitable ocean environment.
Vehicles that communicate their state or measure
the relative state of other vehicles in the team
can cooperate by using feedback control to coordinate their motion as a mobile, reconfigurable
sensor array, responding efficiently to changing
signals, scales, and conditions in the environment. In a variety of contexts, a vehicle team
with judiciously designed cooperative control can
outperform the same team with each vehicle controlled independently. For example, cooperative
control methodologies have been developed to
improve the richness of information in the data
that the vehicles collect, their accuracy in feature
detection and tracking, and the robustness of their
decisions to uncertainty and failures. The chapter
begins with a survey of early work on ocean samp19.1 Motivation .......................................... 441
19.2 Background and History....................... 443
19.3 Advances in Cooperative Vehicle
Ocean Monitoring ................................ 445
19.3.1 Cooperative Gliders in AOSN II ..... 445
19.3.2 Cooperative Gliders in ASAP ........ 448
19.4 Recent Developments
and Future Directions .......................... 452
References................................................... 454
ling and environmental monitoring, cooperative control, and collective motion. The theory,
methodology, and field deployment are then highlighted for two projects on cooperative vehicle
monitoring in the coastal ocean that demonstrated
the applicability and associated performance
advantages of cooperative control. The chapter
concludes with a presentation of more recent
developments as well as future directions in cooperative vehicle environmental monitoring.
19.1 Motivation
Over the last decade, methodologies for automated
cooperative control of robotic vehicles have been designed, deployed, and proven to provide efficient, reliable, and sustained monitoring of the uncertain and
inhospitable ocean environment. Unprecedented data
sets have been collected from deployments of cooperative vehicles in the field, and both real-time and
post-deployment analyses have led to new understanding of the environment. This first decade of success in
cooperative vehicle environmental monitoring sets the
stage for new opportunities and future gain, especially
as the development of cooperative control methodologies can continue to leverage ongoing technological and
scientific advances in underwater communication and
sensing, energy and computational efficiency, vehicle
size, speed, maneuverability and cost, and ocean modeling and prediction.
Indeed, the demonstrated potential of cooperative
vehicle control has led to increased demand for fleets
of autonomous underwater vehicles (AUVs) for use in
measuring ocean physics, biology, chemistry, and geology to improve understanding of natural dynamics
and human-influenced changes in the marine environment. Further, methodologies for cooperative control of
robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there
is much to be learned from developments in these other
domains. The recent explosion in research on networks
and complex systems, including investigation of mechanisms that explain a collective intelligence exhibited by
animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.
For environmental monitoring to be successful,
physical, chemical, and biological variables must be
441
Part B | 19.1
19. Cooperative Vehicle Environmental Monitoring
Naomi Ehrich Leonard
This chapter reviews cooperative control of autonomous vehicles for environmental monitoring
with a focus on methodologies that have been
designed, deployed, and proven to provide efficient, reliable, and sustained monitoring of the
uncertain and inhospitable ocean environment.
Vehicles that communicate their state or measure
the relative state of other vehicles in the team
can cooperate by using feedback control to coordinate their motion as a mobile, reconfigurable
sensor array, responding efficiently to changing
signals, scales, and conditions in the environment. In a variety of contexts, a vehicle team
with judiciously designed cooperative control can
outperform the same team with each vehicle controlled independently. For example, cooperative
control methodologies have been developed to
improve the richness of information in the data
that the vehicles collect, their accuracy in feature
detection and tracking, and the robustness of their
decisions to uncertainty and failures. The chapter
begins with a survey of early work on ocean samp19.1 Motivation .......................................... 441
19.2 Background and History....................... 443
19.3 Advances in Cooperative Vehicle
Ocean Monitoring ................................ 445
19.3.1 Cooperative Gliders in AOSN II ..... 445
19.3.2 Cooperative Gliders in ASAP ........ 448
19.4 Recent Developments
and Future Directions .......................... 452
References................................................... 454
ling and environmental monitoring, cooperative control, and collective motion. The theory,
methodology, and field deployment are then highlighted for two projects on cooperative vehicle
monitoring in the coastal ocean that demonstrated
the applicability and associated performance
advantages of cooperative control. The chapter
concludes with a presentation of more recent
developments as well as future directions in cooperative vehicle environmental monitoring.
19.1 Motivation
Over the last decade, methodologies for automated
cooperative control of robotic vehicles have been designed, deployed, and proven to provide efficient, reliable, and sustained monitoring of the uncertain and
inhospitable ocean environment. Unprecedented data
sets have been collected from deployments of cooperative vehicles in the field, and both real-time and
post-deployment analyses have led to new understanding of the environment. This first decade of success in
cooperative vehicle environmental monitoring sets the
stage for new opportunities and future gain, especially
as the development of cooperative control methodologies can continue to leverage ongoing technological and
scientific advances in underwater communication and
sensing, energy and computational efficiency, vehicle
size, speed, maneuverability and cost, and ocean modeling and prediction.
Indeed, the demonstrated potential of cooperative
vehicle control has led to increased demand for fleets
of autonomous underwater vehicles (AUVs) for use in
measuring ocean physics, biology, chemistry, and geology to improve understanding of natural dynamics
and human-influenced changes in the marine environment. Further, methodologies for cooperative control of
robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there
is much to be learned from developments in these other
domains. The recent explosion in research on networks
and complex systems, including investigation of mechanisms that explain a collective intelligence exhibited by
animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.
For environmental monitoring to be successful,
physical, chemical, and biological variables must be