Part B | 19.4
452 Part B Autonomous Ocean Vehicles, Subsystems and Control
of motion pattern demonstrated the positive impact of
coordinated control on sampling performance. Details
on the ocean conditions, adaptations, and performance
of the gliders during the field experiment are described
in [19.3].
The methodology of [19.6] was successfully implemented in the challenging coastal ocean environment
of Monterey Bay, CA in August 2006, demonstrating
a new capability for ocean sampling. The implementation points to the feasibility and versatility of the
method for adaptation of motion patterns and integrated
human decision making to address a complex multirobot sensing task. The glider network coordination was
autonomous and sustained with glider motion patterns
adapted to meet the requirements of the changing ocean
sampling mission and the changing dynamic state of the
ocean. The methodology can be applied to collaborative
robotic sensing in other domains.
19.4 Recent Developments and Future Directions
Recent years have seen an acceleration of theoretical
developments and field testing of cooperative vehicle
environmental monitoring; these have both motivated
and leveraged advances in sensors, energy efficient
and versatile mobile sensing platforms, communications, environmental modeling and estimation techniques, and control theory for cooperative exploration
by networked mobile agents. It has become clear that
robotic vehicles are especially useful for monitoring
in remote and hostile environments and that their potential is much enhanced when they perform in cooperative teams [19.99]. A recent survey of robotics
for environmental monitoring, including a discussion
of cooperative systems, is provided by Dunbabin and
Marques [19.100]. Redfield surveys works on cooperative underwater vehicles in [19.101]. Many of the recent
advances are opening up further opportunities and avenues for continued research.
Further, new infrastructure developments, such as
the cabled ocean observatories, provide significant resources that could potentially enhance the versatility
of cooperative autonomous vehicles, most particularly
with respect to data, communication, and power. For example, the NEPTUNE regional cabled ocean observatory boasts continuous high-power and high-bandwidth
data transfer in real time between a large expanse of the
ocean sea floor and the shore. Without having to surface, any autonomous vehicle could make use of sea
floor nodes to send its data back to shore, or to share its
location and data with other vehicles in the team, which
would improve coordination among vehicles. A vehicle
could also use the infrastructure to calibrate its location
for its on-board deduced reckoning and to leverage the
data collected in the cabled observed for its on-board
(and thus collective) decision-making. Further, if docking stations were available, autonomous vehicles could
recharge their batteries. Leveraging cabled observatories in this way would be most advantageous if dynamic
sampling coverage were needed to complement the existing coverage from the sea floor nodes. Otherwise,
there would be a tradeoff for the autonomous vehicles
between moving into areas already well covered and exploring new areas where no cabled observatory exists.
Examples of recent field-tested methodologies for
cooperative vehicle environmental monitoring include
a decentralized strategy for coordinated harbor patrol
using the theory of Gaussian processes implemented on
three AUVs in Lisbon harbor by Marino et al. [19.102].
Schofield et al. [19.103] describe a number of field tests
including a test of remote coordination of an array of
acoustically networked AUVs and the coordinated sampling of underwater gliders and the space-based Hyperion imager flying on the Earth Observing-1 spacecraft.
Using decentralized data fusion and control, two UAVs
were used to demonstrate cooperative localization of
ground-based features by Cole et al. [19.104]. Techy
et al. [19.105] implemented a strategy for coordination based on speed modulation to synchronize two
autonomous UAVs for tracking long-distance movement of plant pathogens above crop fields. Maczka
et al. [19.106] demonstrated an efficient method for cooperative navigation of underwater vehicles from timesynchronized acoustic data transmissions. Hollinger
et al. [19.107] demonstrated on a single AUV in the
Southern California Bight a probabilistic planner that
uses uncertainty in ocean current prediction based on
an interpolation variance. Merino et al. [19.108] presented a cooperative perception system for multiple
UAVs with different kinds of sensors and showed experimental results of forest fire detection with cooperating
UAVs. Alvarez et al. [19.109] described methodology
that estimates volumetric distribution of the geostrophic
current field from glider measurements merged with
satellite altimetry data; this methodology was validated
using data collected from three Slocum gliders and
one Spray glider moving along predefined paths during
a field experiment in August 2010 in a coastal region of
the Ligurian Sea. Alvarez and Mourre [19.110] examined optimal sampling strategies for a single underwater
glider sampling in the presence of a mooring.
452 Part B Autonomous Ocean Vehicles, Subsystems and Control
of motion pattern demonstrated the positive impact of
coordinated control on sampling performance. Details
on the ocean conditions, adaptations, and performance
of the gliders during the field experiment are described
in [19.3].
The methodology of [19.6] was successfully implemented in the challenging coastal ocean environment
of Monterey Bay, CA in August 2006, demonstrating
a new capability for ocean sampling. The implementation points to the feasibility and versatility of the
method for adaptation of motion patterns and integrated
human decision making to address a complex multirobot sensing task. The glider network coordination was
autonomous and sustained with glider motion patterns
adapted to meet the requirements of the changing ocean
sampling mission and the changing dynamic state of the
ocean. The methodology can be applied to collaborative
robotic sensing in other domains.
19.4 Recent Developments and Future Directions
Recent years have seen an acceleration of theoretical
developments and field testing of cooperative vehicle
environmental monitoring; these have both motivated
and leveraged advances in sensors, energy efficient
and versatile mobile sensing platforms, communications, environmental modeling and estimation techniques, and control theory for cooperative exploration
by networked mobile agents. It has become clear that
robotic vehicles are especially useful for monitoring
in remote and hostile environments and that their potential is much enhanced when they perform in cooperative teams [19.99]. A recent survey of robotics
for environmental monitoring, including a discussion
of cooperative systems, is provided by Dunbabin and
Marques [19.100]. Redfield surveys works on cooperative underwater vehicles in [19.101]. Many of the recent
advances are opening up further opportunities and avenues for continued research.
Further, new infrastructure developments, such as
the cabled ocean observatories, provide significant resources that could potentially enhance the versatility
of cooperative autonomous vehicles, most particularly
with respect to data, communication, and power. For example, the NEPTUNE regional cabled ocean observatory boasts continuous high-power and high-bandwidth
data transfer in real time between a large expanse of the
ocean sea floor and the shore. Without having to surface, any autonomous vehicle could make use of sea
floor nodes to send its data back to shore, or to share its
location and data with other vehicles in the team, which
would improve coordination among vehicles. A vehicle
could also use the infrastructure to calibrate its location
for its on-board deduced reckoning and to leverage the
data collected in the cabled observed for its on-board
(and thus collective) decision-making. Further, if docking stations were available, autonomous vehicles could
recharge their batteries. Leveraging cabled observatories in this way would be most advantageous if dynamic
sampling coverage were needed to complement the existing coverage from the sea floor nodes. Otherwise,
there would be a tradeoff for the autonomous vehicles
between moving into areas already well covered and exploring new areas where no cabled observatory exists.
Examples of recent field-tested methodologies for
cooperative vehicle environmental monitoring include
a decentralized strategy for coordinated harbor patrol
using the theory of Gaussian processes implemented on
three AUVs in Lisbon harbor by Marino et al. [19.102].
Schofield et al. [19.103] describe a number of field tests
including a test of remote coordination of an array of
acoustically networked AUVs and the coordinated sampling of underwater gliders and the space-based Hyperion imager flying on the Earth Observing-1 spacecraft.
Using decentralized data fusion and control, two UAVs
were used to demonstrate cooperative localization of
ground-based features by Cole et al. [19.104]. Techy
et al. [19.105] implemented a strategy for coordination based on speed modulation to synchronize two
autonomous UAVs for tracking long-distance movement of plant pathogens above crop fields. Maczka
et al. [19.106] demonstrated an efficient method for cooperative navigation of underwater vehicles from timesynchronized acoustic data transmissions. Hollinger
et al. [19.107] demonstrated on a single AUV in the
Southern California Bight a probabilistic planner that
uses uncertainty in ocean current prediction based on
an interpolation variance. Merino et al. [19.108] presented a cooperative perception system for multiple
UAVs with different kinds of sensors and showed experimental results of forest fire detection with cooperating
UAVs. Alvarez et al. [19.109] described methodology
that estimates volumetric distribution of the geostrophic
current field from glider measurements merged with
satellite altimetry data; this methodology was validated
using data collected from three Slocum gliders and
one Spray glider moving along predefined paths during
a field experiment in August 2010 in a coastal region of
the Ligurian Sea. Alvarez and Mourre [19.110] examined optimal sampling strategies for a single underwater
glider sampling in the presence of a mooring.