Part B | 19.3
448 Part B Autonomous Ocean Vehicles, Subsystems and Control
formation size in response to changing scales in the
sampled field.
In the August 6–7 sea trial, the coordinated glider
network measured the front close to its inception, while
in the August 16–17 sea trial, the network measured
the front after it had been advected further to the south
across the mouth of Monterey Bay [19.2]. As a result,
the data collected from these two sea trials added new
insight into the evolving vertical structure of the upwelling plume. Notably, the gradient estimates from the
glider networks would not have been possible using
conventional profiling floats or drifters. Since frontal
dynamics are typically nonlinear, precise gradient estimates are critical in forecasting frontogenesis and
evolving instabilities.
In the third sea trial on August 23, 2003, a single
glider followed a surface drifter in real time, making zigzags across and below its projected path. This
demonstrated yet another opportunity for environmental monitoring with coordinated vehicles, namely that
a glider or glider formation could collect scalar samples and thus estimate gradients both across and along
tracer paths.
19.3.2 Cooperative Gliders in ASAP
In summer 2006, a multidisciplinary research group as
part of the ASAP project performed an unprecedented
field experiment, building on the successful efforts
of the AOSN II project. The ASAP project demonstrated a full-scale adaptive ocean sampling network
featuring a coordinated network of gliders controlled
autonomously over the course of a month to efficiently
sample a 22 40 km
2 and up to more than 1000 m-deep
region of coastal ocean just northwest of Monterey Bay
as shown in Fig. 19.2 [19.3]. The coordinated sampling
of the gliders was integrated with an assortment of additional mobile and stationary sensing platforms, three
real-time numerical ocean models, numerical optimization and prediction tools, a virtual control room, and
a participating team of scientists.
When gliders move without taking into account, for
example, through feedback, the relative position or motion of other gliders in the group, they are susceptible to
strong currents driving them into clumps. In the AOSN
II experiment current-driven clumping was observed
and shown to lead to sensor redundancy with negative
impact on sampling performance [19.6]. This motivated the development of coordinating feedback control
laws that enforce dynamic distribution of vehicles to
enhance sampling performance. The methodology proposed and justified in [19.6] provides this coordinated
feedback control with two components: (1) the design
of coordinated motion patterns for high-performance
sampling and (2) the design of feedback control laws
that systematically and automatically stabilize vehicles
onto the desired coordinated patterns. The methodology of [19.6] was implemented in the 2006 ASAP
experiment, which provided a proof of concept for the
applicability of the methodology to the field.
The methodology proposed in [19.83, 84] was used
for the second component, the systematic generation of
feedback control laws for stable coordination of a network of autonomous vehicles to a family of motion
patterns. The patterns, which consist of vehicles moving on a finite set of closed curves, are distinguished
by a small number of parameters that encode synchrony. For example, two vehicles that move in parallel
around two separate curves have synchronized heading directions, while two vehicles that move around the
same curve but always on opposite sides of the curve
have anti-synchronized heading directions. The corresponding feedback laws the vehicles use are likewise
distinguished by control gains that depend on the same
small number of synchrony parameters.
The control laws are distributed, which means that
each vehicle applies its own control law that depends on
its own measurements. Furthermore, the control laws
are reactive, they do not require a prescription of where
each vehicle should be as a function of time, but rather
each vehicle moves in response to the relative position
and direction of its neighbors. Each vehicle is constantly adjusting what it does to keep moving, to stay
close to its assigned curve, and importantly to maintain
the desired spacing with respect to the other vehicles,
as encoded by the synchrony parameters. Because the
responsive behavior of each individual can be defined
as a function of the state of a small number of other
vehicles, independent of the total number of vehicles,
the control methodology is scalable. And because there
are no leaders or special individuals in the network, the
methodology is robust to vehicle failure.
The GCCS software infrastructure described
in [19.90] and tested in [19.92] was used to implement
the methodology in the field. In the ASAP experiment,
it was observed that vehicles maintain their prescribed
relative spacing in the presence of strong currents
by moving off their assigned curve as needed: when
a vehicle was slowed down by a strong opposing flow
field, it cut inside its curve to make up distance, while
its neighbor on the other side of the curve that was
sped up by the strong current cut outside the curve to
avoid overtaking the slower vehicle and compromising
spacing.
There are several advantages to designing the coordinated motion patterns independently from the design
of control laws to stabilize vehicles to those motion patterns. First, the patterns can be independently chosen to
448 Part B Autonomous Ocean Vehicles, Subsystems and Control
formation size in response to changing scales in the
sampled field.
In the August 6–7 sea trial, the coordinated glider
network measured the front close to its inception, while
in the August 16–17 sea trial, the network measured
the front after it had been advected further to the south
across the mouth of Monterey Bay [19.2]. As a result,
the data collected from these two sea trials added new
insight into the evolving vertical structure of the upwelling plume. Notably, the gradient estimates from the
glider networks would not have been possible using
conventional profiling floats or drifters. Since frontal
dynamics are typically nonlinear, precise gradient estimates are critical in forecasting frontogenesis and
evolving instabilities.
In the third sea trial on August 23, 2003, a single
glider followed a surface drifter in real time, making zigzags across and below its projected path. This
demonstrated yet another opportunity for environmental monitoring with coordinated vehicles, namely that
a glider or glider formation could collect scalar samples and thus estimate gradients both across and along
tracer paths.
19.3.2 Cooperative Gliders in ASAP
In summer 2006, a multidisciplinary research group as
part of the ASAP project performed an unprecedented
field experiment, building on the successful efforts
of the AOSN II project. The ASAP project demonstrated a full-scale adaptive ocean sampling network
featuring a coordinated network of gliders controlled
autonomously over the course of a month to efficiently
sample a 22 40 km
2 and up to more than 1000 m-deep
region of coastal ocean just northwest of Monterey Bay
as shown in Fig. 19.2 [19.3]. The coordinated sampling
of the gliders was integrated with an assortment of additional mobile and stationary sensing platforms, three
real-time numerical ocean models, numerical optimization and prediction tools, a virtual control room, and
a participating team of scientists.
When gliders move without taking into account, for
example, through feedback, the relative position or motion of other gliders in the group, they are susceptible to
strong currents driving them into clumps. In the AOSN
II experiment current-driven clumping was observed
and shown to lead to sensor redundancy with negative
impact on sampling performance [19.6]. This motivated the development of coordinating feedback control
laws that enforce dynamic distribution of vehicles to
enhance sampling performance. The methodology proposed and justified in [19.6] provides this coordinated
feedback control with two components: (1) the design
of coordinated motion patterns for high-performance
sampling and (2) the design of feedback control laws
that systematically and automatically stabilize vehicles
onto the desired coordinated patterns. The methodology of [19.6] was implemented in the 2006 ASAP
experiment, which provided a proof of concept for the
applicability of the methodology to the field.
The methodology proposed in [19.83, 84] was used
for the second component, the systematic generation of
feedback control laws for stable coordination of a network of autonomous vehicles to a family of motion
patterns. The patterns, which consist of vehicles moving on a finite set of closed curves, are distinguished
by a small number of parameters that encode synchrony. For example, two vehicles that move in parallel
around two separate curves have synchronized heading directions, while two vehicles that move around the
same curve but always on opposite sides of the curve
have anti-synchronized heading directions. The corresponding feedback laws the vehicles use are likewise
distinguished by control gains that depend on the same
small number of synchrony parameters.
The control laws are distributed, which means that
each vehicle applies its own control law that depends on
its own measurements. Furthermore, the control laws
are reactive, they do not require a prescription of where
each vehicle should be as a function of time, but rather
each vehicle moves in response to the relative position
and direction of its neighbors. Each vehicle is constantly adjusting what it does to keep moving, to stay
close to its assigned curve, and importantly to maintain
the desired spacing with respect to the other vehicles,
as encoded by the synchrony parameters. Because the
responsive behavior of each individual can be defined
as a function of the state of a small number of other
vehicles, independent of the total number of vehicles,
the control methodology is scalable. And because there
are no leaders or special individuals in the network, the
methodology is robust to vehicle failure.
The GCCS software infrastructure described
in [19.90] and tested in [19.92] was used to implement
the methodology in the field. In the ASAP experiment,
it was observed that vehicles maintain their prescribed
relative spacing in the presence of strong currents
by moving off their assigned curve as needed: when
a vehicle was slowed down by a strong opposing flow
field, it cut inside its curve to make up distance, while
its neighbor on the other side of the curve that was
sped up by the strong current cut outside the curve to
avoid overtaking the slower vehicle and compromising
spacing.
There are several advantages to designing the coordinated motion patterns independently from the design
of control laws to stabilize vehicles to those motion patterns. First, the patterns can be independently chosen to