Cooperative Vehicle Environmental Monitoring 19.2 Background and History 443
Part B | 19.2
Cooperative feedback control makes possible a vehicle network that is autonomous, versatile, and robust
to noise and uncertainty. Further, when each vehicle
has the same feedback law, the vehicle network has
an added robustness to vehicle failures or additions
since an ordering of the vehicles is unnecessary and in
particular the system does not depend on any special individuals. Other promising opportunities have been explored with a heterogeneous group of vehicles. In these
cases, feedback can be used for cooperative subtask
allocation or coordinated complementary actions: for
example, slow-moving autonomous underwater gliders
can provide coverage and fast-moving propelled underwater vehicles can be allocated to relay information
and to move to hot spots at great speed. Similarly, unmanned aerial vehicles (UAVs) can provide large-scale
mapping of sea-surface fields and AUVs can complement with in-depth feature tracking below the surface.
In this chapter, we describe two projects in cooperative vehicle environmental monitoring with extensive
field deployment in the coastal ocean that demonstrated
for the first time at large scale and over several weeks
the applicability and associated performance advantages of cooperative control methodologies for mobile
sensor networks in the ocean. The first of these was
the autonomous ocean sampling network (AOSN) II
project [19.1, 2] with its field experiment in Monterey
Bay, CA, over the month of August 2003 and the second was the adaptive sampling and prediction (ASAP)
project with its field experiment in Monterey Bay, CA,
over the month of August 2006 [19.3].
For AOSN-II, a methodology was designed [19.4]
and demonstrated [19.5] that featured small networks
of autonomous underwater gliders cooperating to compute temperature gradients and track cold upwelled
water. The gliders were also used to demonstrate autonomous coordination of their motion to sample along
the path of drifters and thus increase measurement
density along fronts. For ASAP, a methodology was
designed [19.6] and demonstrated [19.3] that featured
a network of 10 gliders (of two types) to optimize
dynamic sampling coverage, minimizing uncertainty
for estimation of temperature, salinity, and currents in
a large coastal region just north of Monterey Bay. In
both field experiments, three numerical ocean models
ran in near real time, assimilating data collected by
the gliders and providing estimates and predictions that
were used in the gliders’ adaptive motion planning.
Additional real-time data were provided by a research
aircraft, satellite imagery, high-frequency radar, moorings, drifters, and propelled vehicles. In the ASAP
experiment, the cooperative behavior of a network of
six gliders ran autonomously without failure for almost
24 days straight.
An important factor in the success of the AOSNII and the ASAP projects was the strong multidisciplinary collaboration among researchers with expertise
in ocean science, vehicle dynamics, and control and dynamical systems theory. The methodologies developed
drew inspiration from earlier work in ocean sampling
and environmental monitoring, cooperative control, and
collective motion; we present background and history
on these subjects in Sect. 19.2. The AOSN-II and
ASAP programs in cooperative vehicle ocean monitoring, from theory through full-scale ocean deployment,
are reviewed in Sect. 19.3. More recent developments
and future directions in cooperative vehicle environmental monitoring are described in Sect. 19.4.
19.2 Background and History
The autonomous oceanographic sampling network
(AOSN) was introduced in 1993 by Curtin et al. [19.7]
as an approach for dynamic measurement of the ocean
environment and resolution of spatial and temporal gradients in the sampled fields. At that time most oceanographic data were collected from satellites, ships with
towed underwater profilers, and arrays of moorings and
floats. The AOSN concept was to deploy AUVs to
take measurements that would complement those from
distributed point sensors such as moorings and from
remote sensors such as satellites, and enable adaptive
sampling to improve forecast skill; the AOSN system
would operate successfully with the use of acoustic and
radio modems for communication and docking stations
for recharging AUV batteries. It was anticipated that
control would be critical for the AUVs to accomplish
complex missions in the presence of uncertainties and
real-world constraints, and that coordinated control of
the multiple vehicles would lead to system efficiency
and endurance. Bellingham described in [19.8] how
nested approaches would allow sampling the ocean over
a range of spatial and temporal scales.
The vision of an integrated ocean monitoring
system was made possible with the development
of small, relatively inexpensive AUVs. Propelled
AUVs such as the autonomous benthic explorer
(ABE) [19.9], the Odyssey [19.10], and the REMUS [19.11] were designed to provide maneuverability
and speed. Buoyancy-driven autonomous underwater
gliders, including the Slocum [19.12], Spray [19.13]
Part B | 19.2
Cooperative feedback control makes possible a vehicle network that is autonomous, versatile, and robust
to noise and uncertainty. Further, when each vehicle
has the same feedback law, the vehicle network has
an added robustness to vehicle failures or additions
since an ordering of the vehicles is unnecessary and in
particular the system does not depend on any special individuals. Other promising opportunities have been explored with a heterogeneous group of vehicles. In these
cases, feedback can be used for cooperative subtask
allocation or coordinated complementary actions: for
example, slow-moving autonomous underwater gliders
can provide coverage and fast-moving propelled underwater vehicles can be allocated to relay information
and to move to hot spots at great speed. Similarly, unmanned aerial vehicles (UAVs) can provide large-scale
mapping of sea-surface fields and AUVs can complement with in-depth feature tracking below the surface.
In this chapter, we describe two projects in cooperative vehicle environmental monitoring with extensive
field deployment in the coastal ocean that demonstrated
for the first time at large scale and over several weeks
the applicability and associated performance advantages of cooperative control methodologies for mobile
sensor networks in the ocean. The first of these was
the autonomous ocean sampling network (AOSN) II
project [19.1, 2] with its field experiment in Monterey
Bay, CA, over the month of August 2003 and the second was the adaptive sampling and prediction (ASAP)
project with its field experiment in Monterey Bay, CA,
over the month of August 2006 [19.3].
For AOSN-II, a methodology was designed [19.4]
and demonstrated [19.5] that featured small networks
of autonomous underwater gliders cooperating to compute temperature gradients and track cold upwelled
water. The gliders were also used to demonstrate autonomous coordination of their motion to sample along
the path of drifters and thus increase measurement
density along fronts. For ASAP, a methodology was
designed [19.6] and demonstrated [19.3] that featured
a network of 10 gliders (of two types) to optimize
dynamic sampling coverage, minimizing uncertainty
for estimation of temperature, salinity, and currents in
a large coastal region just north of Monterey Bay. In
both field experiments, three numerical ocean models
ran in near real time, assimilating data collected by
the gliders and providing estimates and predictions that
were used in the gliders’ adaptive motion planning.
Additional real-time data were provided by a research
aircraft, satellite imagery, high-frequency radar, moorings, drifters, and propelled vehicles. In the ASAP
experiment, the cooperative behavior of a network of
six gliders ran autonomously without failure for almost
24 days straight.
An important factor in the success of the AOSNII and the ASAP projects was the strong multidisciplinary collaboration among researchers with expertise
in ocean science, vehicle dynamics, and control and dynamical systems theory. The methodologies developed
drew inspiration from earlier work in ocean sampling
and environmental monitoring, cooperative control, and
collective motion; we present background and history
on these subjects in Sect. 19.2. The AOSN-II and
ASAP programs in cooperative vehicle ocean monitoring, from theory through full-scale ocean deployment,
are reviewed in Sect. 19.3. More recent developments
and future directions in cooperative vehicle environmental monitoring are described in Sect. 19.4.
19.2 Background and History
The autonomous oceanographic sampling network
(AOSN) was introduced in 1993 by Curtin et al. [19.7]
as an approach for dynamic measurement of the ocean
environment and resolution of spatial and temporal gradients in the sampled fields. At that time most oceanographic data were collected from satellites, ships with
towed underwater profilers, and arrays of moorings and
floats. The AOSN concept was to deploy AUVs to
take measurements that would complement those from
distributed point sensors such as moorings and from
remote sensors such as satellites, and enable adaptive
sampling to improve forecast skill; the AOSN system
would operate successfully with the use of acoustic and
radio modems for communication and docking stations
for recharging AUV batteries. It was anticipated that
control would be critical for the AUVs to accomplish
complex missions in the presence of uncertainties and
real-world constraints, and that coordinated control of
the multiple vehicles would lead to system efficiency
and endurance. Bellingham described in [19.8] how
nested approaches would allow sampling the ocean over
a range of spatial and temporal scales.
The vision of an integrated ocean monitoring
system was made possible with the development
of small, relatively inexpensive AUVs. Propelled
AUVs such as the autonomous benthic explorer
(ABE) [19.9], the Odyssey [19.10], and the REMUS [19.11] were designed to provide maneuverability
and speed. Buoyancy-driven autonomous underwater
gliders, including the Slocum [19.12], Spray [19.13]