Cooperative Vehicle Environmental Monitoring 19.3 Advances in Cooperative Vehicle Ocean Monitoring 445
Part B | 19.3
vehicle groups could cooperate to generate contour
plots of noisy, unknown fields, adjusting their formation shape to provide optimal filtering of their noisy
measurements. Related work addressed environmental
boundary tracking [19.67, 68], coverage control [19.69,
70], target tracking [19.71, 72], and maximization of information [19.50].
Researchers in control also took a strong interest
in the dynamics of consensus within a network; the
topology of the sensing and communication interconnections among agents was encoded using graphs, and
the convergence of consensus dynamics was proved
with approaches that exploited graph theory allowing for time-varying communication graphs and timedelayed communications [19.73–77]. Consensus in the
positions of agents was used to address a variety of
other problems including formations [19.78] and rendezvous [19.79].
In most of this consensus literature the dynamics are
linear, and yet the problem of consensus on direction
of motion that mobile robots must make is nonlinear,
since the space of directions in the plane is a circle
(and not a line). Consensus on the circle is called synchronization, and it has been studied extensively in the
context of coupled phase oscillators used to model a variety of interconnected periodic processes in biology
and physics (firefly flashing and neuron firing) [19.80,
81]. Justh and Krishnaprasad developed a geometric
framework to design steering control laws to coordinate the motion of vehicles in [19.82]. This approach
was generalized in the work of Sepulchre et al. [19.83,
84] using a model that extends coupled oscillator dynamics, in which the phase of each oscillator represents
the direction of motion of a vehicle, to include the spatial dimensions, which represent the positions of the
vehicles. These works provided a systematic methodology for designing provable, distributed control laws
that stabilize motion patterns in the plane ([19.85–87]
for 3-D); each vehicle uses a feedback law that depends
only on what limited measurements it can make, and the
controlled system can cope with a time-varying communication network and with real-time changes in the
number of vehicles in the group. Because the methodology is systematic and robust and because distributed
control of vehicle motion patterns is central to environmental monitoring, the methodology of [19.83, 84] was
developed into an adaptive sampling methodology for
mobile sensor networks in the ocean [19.6] (Sect. 19.3.2
below reviews the implementation of this methodology
in the field).
19.3 Advances in Cooperative Vehicle Ocean Monitoring
The AOSN II and ASAP projects were driven by
an interest in developing sustainable, portable, adaptive ocean observing and prediction systems for use
in coastal environments. The projects used cooperating
AUVs carrying sensors to measure the physics and biology in the ocean together with advanced ocean models
in an effort to improve the ability to observe and predict ocean dynamics. A central focus was on reliable,
efficient and adaptive coordinated control strategies for
mobile sensor platforms to collect data of high value.
Both the AOSN II and ASAP experiments were designed to bring together new techniques in sensing,
forecasting and coordinated control; see [19.88] for
a summary of goals and progress. The 2003 AOSN II
experiment brought these techniques together for the
first time, yielding an unprecedented data set. The 2006
ASAP experiment fully integrated these techniques to
even greater benefit, demonstrating their potential in
a versatile and high-performing adaptive coastal ocean
observing and prediction system. The methodologies
derived, integrated, and demonstrated are adaptable to
a wide variety of environmental monitoring problems
and settings.
19.3.1 Cooperative Gliders in AOSN II
In summer 2003, a multidisciplinary research group as
part of the AOSN II project produced an unprecedented
in-situ observational capability for studying upwelling
features in Monterey Bay over the course of a monthlong field experiment [19.1, 2]. A highlight was the
simultaneous deployment of more than a dozen, sensorequipped, autonomous underwater gliders [19.15], including 5 Spray gliders (Scripps Institution of Oceanography, La Jolla, CA) and up to 10 Slocum gliders (Woods Hole Oceanographic Institution (WHOI),
Woods Hole, MA). Autonomous underwater gliders are
high endurance, buoyancy-driven vehicles that move up
and down in the ocean by controlling their net buoyancy
using pumping systems. Their fixed wings and tail give
them lift, which helps them make forward progress by
following sawtooth-shaped trajectories. To control their
attitude, gliders actively redistribute internal mass. The
Slocum uses a rudder to control heading, and the Spray
shifts mass to the side to roll, bank, and turn. During the field experiment, the gliders were operated to
achieve a fixed velocity relative to the flow. Their effec-
Part B | 19.3
vehicle groups could cooperate to generate contour
plots of noisy, unknown fields, adjusting their formation shape to provide optimal filtering of their noisy
measurements. Related work addressed environmental
boundary tracking [19.67, 68], coverage control [19.69,
70], target tracking [19.71, 72], and maximization of information [19.50].
Researchers in control also took a strong interest
in the dynamics of consensus within a network; the
topology of the sensing and communication interconnections among agents was encoded using graphs, and
the convergence of consensus dynamics was proved
with approaches that exploited graph theory allowing for time-varying communication graphs and timedelayed communications [19.73–77]. Consensus in the
positions of agents was used to address a variety of
other problems including formations [19.78] and rendezvous [19.79].
In most of this consensus literature the dynamics are
linear, and yet the problem of consensus on direction
of motion that mobile robots must make is nonlinear,
since the space of directions in the plane is a circle
(and not a line). Consensus on the circle is called synchronization, and it has been studied extensively in the
context of coupled phase oscillators used to model a variety of interconnected periodic processes in biology
and physics (firefly flashing and neuron firing) [19.80,
81]. Justh and Krishnaprasad developed a geometric
framework to design steering control laws to coordinate the motion of vehicles in [19.82]. This approach
was generalized in the work of Sepulchre et al. [19.83,
84] using a model that extends coupled oscillator dynamics, in which the phase of each oscillator represents
the direction of motion of a vehicle, to include the spatial dimensions, which represent the positions of the
vehicles. These works provided a systematic methodology for designing provable, distributed control laws
that stabilize motion patterns in the plane ([19.85–87]
for 3-D); each vehicle uses a feedback law that depends
only on what limited measurements it can make, and the
controlled system can cope with a time-varying communication network and with real-time changes in the
number of vehicles in the group. Because the methodology is systematic and robust and because distributed
control of vehicle motion patterns is central to environmental monitoring, the methodology of [19.83, 84] was
developed into an adaptive sampling methodology for
mobile sensor networks in the ocean [19.6] (Sect. 19.3.2
below reviews the implementation of this methodology
in the field).
19.3 Advances in Cooperative Vehicle Ocean Monitoring
The AOSN II and ASAP projects were driven by
an interest in developing sustainable, portable, adaptive ocean observing and prediction systems for use
in coastal environments. The projects used cooperating
AUVs carrying sensors to measure the physics and biology in the ocean together with advanced ocean models
in an effort to improve the ability to observe and predict ocean dynamics. A central focus was on reliable,
efficient and adaptive coordinated control strategies for
mobile sensor platforms to collect data of high value.
Both the AOSN II and ASAP experiments were designed to bring together new techniques in sensing,
forecasting and coordinated control; see [19.88] for
a summary of goals and progress. The 2003 AOSN II
experiment brought these techniques together for the
first time, yielding an unprecedented data set. The 2006
ASAP experiment fully integrated these techniques to
even greater benefit, demonstrating their potential in
a versatile and high-performing adaptive coastal ocean
observing and prediction system. The methodologies
derived, integrated, and demonstrated are adaptable to
a wide variety of environmental monitoring problems
and settings.
19.3.1 Cooperative Gliders in AOSN II
In summer 2003, a multidisciplinary research group as
part of the AOSN II project produced an unprecedented
in-situ observational capability for studying upwelling
features in Monterey Bay over the course of a monthlong field experiment [19.1, 2]. A highlight was the
simultaneous deployment of more than a dozen, sensorequipped, autonomous underwater gliders [19.15], including 5 Spray gliders (Scripps Institution of Oceanography, La Jolla, CA) and up to 10 Slocum gliders (Woods Hole Oceanographic Institution (WHOI),
Woods Hole, MA). Autonomous underwater gliders are
high endurance, buoyancy-driven vehicles that move up
and down in the ocean by controlling their net buoyancy
using pumping systems. Their fixed wings and tail give
them lift, which helps them make forward progress by
following sawtooth-shaped trajectories. To control their
attitude, gliders actively redistribute internal mass. The
Slocum uses a rudder to control heading, and the Spray
shifts mass to the side to roll, bank, and turn. During the field experiment, the gliders were operated to
achieve a fixed velocity relative to the flow. Their effec-