Part B | 19.1
442 Part B Autonomous Ocean Vehicles, Subsystems and Control
measured across a range of spatial and temporal scales;
in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space
that is highly uncertain and dynamic. Small spatial and
temporal scales associated with the measured variables
typically make a stationary sensor array impractical because a very large number of sensors would be needed
to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited
to such a challenge since mobility can be exploited to
dynamically distribute fewer sensors according to the
spatial and temporal scales.
The underlying principle of cooperative control of
vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among
the individual sensors to yield a collective behavior
that performs better than the sum of the parts. If the
vehicles can communicate their state or measure the
relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide
coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically
reconfigurable sensor array with a capability for high
performance in environmental monitoring not available
at the level of individuals. High performance has been
demonstrated with cooperative vehicle groups in the
ocean in terms of richness of information in measurements, accuracy in feature detection and tracking, and
robustness of decisions to uncertainty and failures.
Methodologies for systematic generation of feedback control laws that yield provable collective dynamics have been critical to the successful design of
high-performing cooperative vehicle networks. Feedback control refers here to the automated changes that
each vehicle makes in response to its measurements of
the sampled fields, the relative state of other vehicles in
the network, and any additional available measured or
computed signals.
Consider, for example, the task of tracking highdensity phytoplankton patches in the ocean with a team
of AUVs that carry optical sensors for measuring phytoplankton concentration. Suppose also that when any
two vehicles in the team are not separated by too great
a distance (call them neighbors), they can measure or
communicate to each other their relative position and/or
velocity. Then, each vehicle can apply a feedback control law, at its sampling or communication frequency,
that moves it in the direction of a combination of its
best estimate of:
1. The direction of increasing concentration
2. The direction toward its neighbors that are farther
than a prescribed separation distance
3. The direction away from its neighbors that
are closer than a prescribed minimal separation
distance.
In ideal conditions, the vehicles will move as a regularly spaced array up the phytoplankton concentration
gradient; in real conditions, feedback will provide robustness to noise, uncertainty, and disturbance within
bounds. An augmentation to this feedback law to further reduce error due to noisy measurements has each
vehicle compute a local estimate of the optimal vehicle array resolution for gradient climbing accuracy and
then adapt the prescribed separation distances between
neighbors to achieve this resolution.
Consider, as another example, the task of providing
dynamic sampling coverage of the changing phytoplankton patches over a fixed region with this same
team of AUVs. The goal is to enable the vehicles to efficiently sample the patches across the region so that the
data can be used to map the phytoplankton patches with
minimal mapping error. Each vehicle can apply a feedback control law in response to where its neighbors are
collecting data and in accordance with priors on spatial
and temporal scales associated with the phytoplankton
patches. The feedback control law moves it toward a location that is easily accessible, is away from others,
and has not been recently sampled. In this case in ideal
conditions, the vehicle network will cooperatively perform dynamic sampling coverage of the patches over
the region; in real conditions, feedback will provide robustness to some noise, uncertainty, and disturbance.
An augmentation to this feedback law has each vehicle
compute a local estimate of changing spatial and temporal scales to update its priors and adapt how far from
other vehicles to move and how frequently to re-sample
previously sampled locations.
Feedback has also been used to close the loop
between cooperating vehicle networks and advanced
ocean models when data collected by the vehicle network can be made available for assimilation into the
ocean models and the ocean model predictions can be
made available to one or more of the vehicles. In the
first example of tracking high-density phytoplankton
patches, the feedback with ocean models allows individual vehicles to modify their gradient climb based
on predictions of high-density locations. In the second example of dynamic sampling coverage in a fixed
region, the feedback with ocean models provides enhanced estimates of uncertainty so that individuals
could bias their motion toward sampling locations with
the greatest possibility of minimizing uncertainty in the
mapping. Forecasts of ocean currents are also useful for
navigation.
442 Part B Autonomous Ocean Vehicles, Subsystems and Control
measured across a range of spatial and temporal scales;
in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space
that is highly uncertain and dynamic. Small spatial and
temporal scales associated with the measured variables
typically make a stationary sensor array impractical because a very large number of sensors would be needed
to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited
to such a challenge since mobility can be exploited to
dynamically distribute fewer sensors according to the
spatial and temporal scales.
The underlying principle of cooperative control of
vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among
the individual sensors to yield a collective behavior
that performs better than the sum of the parts. If the
vehicles can communicate their state or measure the
relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide
coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically
reconfigurable sensor array with a capability for high
performance in environmental monitoring not available
at the level of individuals. High performance has been
demonstrated with cooperative vehicle groups in the
ocean in terms of richness of information in measurements, accuracy in feature detection and tracking, and
robustness of decisions to uncertainty and failures.
Methodologies for systematic generation of feedback control laws that yield provable collective dynamics have been critical to the successful design of
high-performing cooperative vehicle networks. Feedback control refers here to the automated changes that
each vehicle makes in response to its measurements of
the sampled fields, the relative state of other vehicles in
the network, and any additional available measured or
computed signals.
Consider, for example, the task of tracking highdensity phytoplankton patches in the ocean with a team
of AUVs that carry optical sensors for measuring phytoplankton concentration. Suppose also that when any
two vehicles in the team are not separated by too great
a distance (call them neighbors), they can measure or
communicate to each other their relative position and/or
velocity. Then, each vehicle can apply a feedback control law, at its sampling or communication frequency,
that moves it in the direction of a combination of its
best estimate of:
1. The direction of increasing concentration
2. The direction toward its neighbors that are farther
than a prescribed separation distance
3. The direction away from its neighbors that
are closer than a prescribed minimal separation
distance.
In ideal conditions, the vehicles will move as a regularly spaced array up the phytoplankton concentration
gradient; in real conditions, feedback will provide robustness to noise, uncertainty, and disturbance within
bounds. An augmentation to this feedback law to further reduce error due to noisy measurements has each
vehicle compute a local estimate of the optimal vehicle array resolution for gradient climbing accuracy and
then adapt the prescribed separation distances between
neighbors to achieve this resolution.
Consider, as another example, the task of providing
dynamic sampling coverage of the changing phytoplankton patches over a fixed region with this same
team of AUVs. The goal is to enable the vehicles to efficiently sample the patches across the region so that the
data can be used to map the phytoplankton patches with
minimal mapping error. Each vehicle can apply a feedback control law in response to where its neighbors are
collecting data and in accordance with priors on spatial
and temporal scales associated with the phytoplankton
patches. The feedback control law moves it toward a location that is easily accessible, is away from others,
and has not been recently sampled. In this case in ideal
conditions, the vehicle network will cooperatively perform dynamic sampling coverage of the patches over
the region; in real conditions, feedback will provide robustness to some noise, uncertainty, and disturbance.
An augmentation to this feedback law has each vehicle
compute a local estimate of changing spatial and temporal scales to update its priors and adapt how far from
other vehicles to move and how frequently to re-sample
previously sampled locations.
Feedback has also been used to close the loop
between cooperating vehicle networks and advanced
ocean models when data collected by the vehicle network can be made available for assimilation into the
ocean models and the ocean model predictions can be
made available to one or more of the vehicles. In the
first example of tracking high-density phytoplankton
patches, the feedback with ocean models allows individual vehicles to modify their gradient climb based
on predictions of high-density locations. In the second example of dynamic sampling coverage in a fixed
region, the feedback with ocean models provides enhanced estimates of uncertainty so that individuals
could bias their motion toward sampling locations with
the greatest possibility of minimizing uncertainty in the
mapping. Forecasts of ocean currents are also useful for
navigation.