Part B | 19.3
446 Part B Autonomous Ocean Vehicles, Subsystems and Control
tive forward speed was of the same order as the stronger
currents in and around Monterey Bay: 25 cm s
1 in
the case of Spray and 35 cm s
1 in the case of Slocum.
Thus, when the currents were too strong, the gliders
did not make progress if they were moving against the
currents.
As part of the field experiment, sea trials were run
with groups of three Slocum gliders controlled into triangular formations [19.5]; see also [19.6] for a survey.
Feature-tracking capabilities of the glider formations
were demonstrated under the challenging conditions of
limited communication and limited feedback as well as
a strong flow field. Two sea trials tested strategies for
cooperative motion control and cooperative gradient estimation for the gliders at relatively small scales in the
region, i. e., on the order of 3 km. In a third sea trial,
a Slocum glider was used to track a Lagrangian drifter
in real time and collect data in a volume surrounding
the path of the drifter. This demonstrated the utility of
a glider, and the possibilities for a network of gliders, to
track Lagrangian particle features such as a water mass
encompassing an algal bloom [19.5].
The Slocum gliders were operated to 200 m depth
and were deployed far enough from the coast to avoid
shallower water. Each glider surfaced every couple
of hours, although the gliders did not surface synchronously. At the surface, each glider got a global
positioning system (GPS) fix for navigation, and using Iridium satellite and ethernet, sent back to shore
the data it had collected and received updated mission
commands from the shore computer. Since the gliders
were not equipped with underwater communication, the
communication with the shore computers was the only
means for (indirect) communication between gliders.
The strategy for coordinated motion control was
based on the virtual body and artificial potential
(VBAP) methodology for control of multiple vehicles
described in [19.4]. VBAP is a general coordinated
control strategy that stabilizes the translation, rotation,
and expansion of a formation of autonomous vehicles;
it is especially well suited to missions that require
a changeable mobile sensor array such as gradient
climbing in a scalar, sampled field, where expansion
and contraction of the network modify the resolution
of the array. The virtual body refers to a collection
of moving reference points, each with dynamics that
are computed centrally and made available to the autonomous vehicles. Spring-like control forces for the
vehicles, and the virtual body, are derived from artificial potentials between the vehicles and the virtual
body; they are designed to stabilize the dynamics of
the vehicles and the virtual body into a formation. In
the computation of its control law, each vehicle uses
a measurement of the relative position of neighboring vehicles and nearby reference points on the virtual
body.
The local gradients of a sampled scalar field can be
estimated on-board or centrally, if the samples taken onboard the vehicles can be communicated among them
or to a central computer. The group will control its
motion in the direction of the gradient if the virtual
body dynamics are designed to move it in the direction of the gradient; for example, if the dynamics of the
virtual body move it toward the coldest water, as determined from an estimated temperature gradient, the
vehicle group will move toward the coldest water as
well since it moves with the virtual body in formation.
In the VBAP methodology, stability and convergence
of the vehicle formation is guaranteed with a feedback
control on the speed of the virtual body.
Ideal assumptions, including continuous communication and feedback for the autonomous vehicles, were
used to prove the control theory and algorithms described in [19.4]. Thus, in order to make the control
methodology applicable to the conditions of the cooperative control sea trials of 2003, the VBAP control
methodology was modified in a few key ways. For example, the algorithms were integrated with the on-board
glider waypoint tracking routine and adjusted to accommodate the constant speed of the gliders, the high-speed
ocean currents, the asynchronous surfacings of the gliders, as well as other latencies [19.89]. The approach was
later systematized as part of the ASAP project in a fully
automated software suite, the glider coordinated control
system (GCCS) [19.90]. The GCCS automates the decentralized coordinated control methodology of [19.6]
for adaptive sampling motion patterns.
The main idea behind the integration of VBAP with
waypoint control was to use VBAP to produce waypoint
lists, corresponding to coordinated glider trajectories,
that the gliders would then follow using their on-board
waypoint control [19.5, 89]. Accordingly, VBAP was
run in a planning mode using a simulation of the gliders with initial conditions defined by the gliders’ most
recent GPS fixes and average flow measurements. The
VBAP-planned trajectories were discretized into waypoint lists. Each Slocum glider uploaded its waypoint
list when it surfaced, and followed those waypoints for
the subsequent 2 h until its next surfacing.
For the Slocum vehicles, a waypoint is prescribed
as a vertical cylinder in the ocean since it refers to a position in the horizontal plane and a radius that sets how
close the vehicle should come to the waypoint position.
Thus, when a sequence of waypoints is prescribed, the
glider follows the waypoints by passing through each of
the corresponding cylinders in the prescribed sequence.
To follow a sequence of waypoints, each glider uses
its on-board low-level heading control which depends
446 Part B Autonomous Ocean Vehicles, Subsystems and Control
tive forward speed was of the same order as the stronger
currents in and around Monterey Bay: 25 cm s
1 in
the case of Spray and 35 cm s
1 in the case of Slocum.
Thus, when the currents were too strong, the gliders
did not make progress if they were moving against the
currents.
As part of the field experiment, sea trials were run
with groups of three Slocum gliders controlled into triangular formations [19.5]; see also [19.6] for a survey.
Feature-tracking capabilities of the glider formations
were demonstrated under the challenging conditions of
limited communication and limited feedback as well as
a strong flow field. Two sea trials tested strategies for
cooperative motion control and cooperative gradient estimation for the gliders at relatively small scales in the
region, i. e., on the order of 3 km. In a third sea trial,
a Slocum glider was used to track a Lagrangian drifter
in real time and collect data in a volume surrounding
the path of the drifter. This demonstrated the utility of
a glider, and the possibilities for a network of gliders, to
track Lagrangian particle features such as a water mass
encompassing an algal bloom [19.5].
The Slocum gliders were operated to 200 m depth
and were deployed far enough from the coast to avoid
shallower water. Each glider surfaced every couple
of hours, although the gliders did not surface synchronously. At the surface, each glider got a global
positioning system (GPS) fix for navigation, and using Iridium satellite and ethernet, sent back to shore
the data it had collected and received updated mission
commands from the shore computer. Since the gliders
were not equipped with underwater communication, the
communication with the shore computers was the only
means for (indirect) communication between gliders.
The strategy for coordinated motion control was
based on the virtual body and artificial potential
(VBAP) methodology for control of multiple vehicles
described in [19.4]. VBAP is a general coordinated
control strategy that stabilizes the translation, rotation,
and expansion of a formation of autonomous vehicles;
it is especially well suited to missions that require
a changeable mobile sensor array such as gradient
climbing in a scalar, sampled field, where expansion
and contraction of the network modify the resolution
of the array. The virtual body refers to a collection
of moving reference points, each with dynamics that
are computed centrally and made available to the autonomous vehicles. Spring-like control forces for the
vehicles, and the virtual body, are derived from artificial potentials between the vehicles and the virtual
body; they are designed to stabilize the dynamics of
the vehicles and the virtual body into a formation. In
the computation of its control law, each vehicle uses
a measurement of the relative position of neighboring vehicles and nearby reference points on the virtual
body.
The local gradients of a sampled scalar field can be
estimated on-board or centrally, if the samples taken onboard the vehicles can be communicated among them
or to a central computer. The group will control its
motion in the direction of the gradient if the virtual
body dynamics are designed to move it in the direction of the gradient; for example, if the dynamics of the
virtual body move it toward the coldest water, as determined from an estimated temperature gradient, the
vehicle group will move toward the coldest water as
well since it moves with the virtual body in formation.
In the VBAP methodology, stability and convergence
of the vehicle formation is guaranteed with a feedback
control on the speed of the virtual body.
Ideal assumptions, including continuous communication and feedback for the autonomous vehicles, were
used to prove the control theory and algorithms described in [19.4]. Thus, in order to make the control
methodology applicable to the conditions of the cooperative control sea trials of 2003, the VBAP control
methodology was modified in a few key ways. For example, the algorithms were integrated with the on-board
glider waypoint tracking routine and adjusted to accommodate the constant speed of the gliders, the high-speed
ocean currents, the asynchronous surfacings of the gliders, as well as other latencies [19.89]. The approach was
later systematized as part of the ASAP project in a fully
automated software suite, the glider coordinated control
system (GCCS) [19.90]. The GCCS automates the decentralized coordinated control methodology of [19.6]
for adaptive sampling motion patterns.
The main idea behind the integration of VBAP with
waypoint control was to use VBAP to produce waypoint
lists, corresponding to coordinated glider trajectories,
that the gliders would then follow using their on-board
waypoint control [19.5, 89]. Accordingly, VBAP was
run in a planning mode using a simulation of the gliders with initial conditions defined by the gliders’ most
recent GPS fixes and average flow measurements. The
VBAP-planned trajectories were discretized into waypoint lists. Each Slocum glider uploaded its waypoint
list when it surfaced, and followed those waypoints for
the subsequent 2 h until its next surfacing.
For the Slocum vehicles, a waypoint is prescribed
as a vertical cylinder in the ocean since it refers to a position in the horizontal plane and a radius that sets how
close the vehicle should come to the waypoint position.
Thus, when a sequence of waypoints is prescribed, the
glider follows the waypoints by passing through each of
the corresponding cylinders in the prescribed sequence.
To follow a sequence of waypoints, each glider uses
its on-board low-level heading control which depends