Friday, October 8, 2010
Saturday, October 2, 2010
Sensing networks and beyond
Sensing networks and beyond
The elements previously described are the most immediate
requirements for unmanned platforms in the underwater domain.
However, other long-term subsea applications are appearing
posing other problems and challenges. The planning
community can help to overcome them.
Ocean observatories in oceanology, all year round subsea
field inspection for the energy industry and continous
harbour and coast patroling for the Navy are emerging, requiring
a more permanent presence of robotic and sensing
tools underwater. These applications are demanding underwater
networks of fixed sensors in combination with fleets
of AUVs and gliders. These sensing webs require decision
making algorithms in order to be able to optimize the management
of heterogeneous assets and resources, to couple
observation systems and ocean models, to minimize error
in the reconstruction of ocean fields and to provide fast dynamic
response to events.
The elements previously described are the most immediate
requirements for unmanned platforms in the underwater domain.
However, other long-term subsea applications are appearing
posing other problems and challenges. The planning
community can help to overcome them.
Ocean observatories in oceanology, all year round subsea
field inspection for the energy industry and continous
harbour and coast patroling for the Navy are emerging, requiring
a more permanent presence of robotic and sensing
tools underwater. These applications are demanding underwater
networks of fixed sensors in combination with fleets
of AUVs and gliders. These sensing webs require decision
making algorithms in order to be able to optimize the management
of heterogeneous assets and resources, to couple
observation systems and ocean models, to minimize error
in the reconstruction of ocean fields and to provide fast dynamic
response to events.
Collaborative planning
Collaborative planning
Mission plan adaptation can be extended to multiple platforms.
The potential benefits of multi-vehicle operations
include force-multiplier effects, redundancy avoidance and
the utilisation of heterogeneous vehicle configurations to reduce
risk to expensive assets. But to achieve the full benefits,
suitable architectures that facilitate true cooperative autonomous
planning for task splitting and allocation must be
realised (Evans et al. 2006).
In this case, it is necessary to extend the single vehicle
mission plan recovery to a mission plan recovery for a group
or team of vehicles performing a collaborative mission. A
shared situation awareness is required for the team of vehicles
(SAT ) to which every team member possess the SAS
required for its responsibilities (Cartwright et al. 2007). The
main challenge in this case is to deal with the acoustic communication
limitations associated to the underwater environment
(Sotzing, Evans, and Lane 2007).
Collaborative planning
Collaborative planning
Mission plan adaptation can be extended to multiple platforms.
The potential benefits of multi-vehicle operations
include force-multiplier effects, redundancy avoidance and
the utilisation of heterogeneous vehicle configurations to reduce
risk to expensive assets. But to achieve the full benefits,
suitable architectures that facilitate true cooperative autonomous
planning for task splitting and allocation must be
realised (Evans et al. 2006).
In this case, it is necessary to extend the single vehicle
mission plan recovery to a mission plan recovery for a group
or team of vehicles performing a collaborative mission. A
shared situation awareness is required for the team of vehicles
(SAT ) to which every team member possess the SAS
required for its responsibilities (Cartwright et al. 2007). The
main challenge in this case is to deal with the acoustic communication
limitations associated to the underwater environment
(Sotzing, Evans, and Lane 2007).
Mission planning
Mission planning
Single platform
Current mission plan solutions for underwater platforms
are procedural and static (Hagen 2001). If behaviors are
added (Pang et al. 2003), they are only to cope with possible
changes that are known a-priori by the operator. In order
to achieve higher levels of autonomy, plan adaptability
should be available not only to procedural waypoint-based
approaches on trajectory planning but also to a declarative
goal-based solution for adaptive mission planning.
On a goal-oriented command and control, decision tools
can be developed providing support for adaptive data sampling,
optimization of resources and fault recovery capabilities
of the platform (Bellingham, Kirkwood, and Rajan
2006).
The potential benefits of the planning capabilities for
adaptive data sampling have been studied by Rajan (Rajan et
al. 2007) and Fox (Fox et al. 2007). An example of this application
is the T-Rex system, an adaptive planning architecture
with notions of timing and onboard resource management
responsiveness to opportunistic science events (Mc-
Gann et al. 2007).
In a similar way, fault-tolerant mission planning adaptation
can also contribute towards providing more mission
flexibility and robustness. When active and passive measures
fail to protect the vehicle, or unexpected hardware failures
occur, the focus of the mission should shift to ’reconfigure’
itself to use alternative combinations of the remaining
resources (see Fig. 9). In the underwater environment, autonomous
embedded recoverability, is a key capability for
vehicle’s endurance. This can be achieved via adaptation of
the vehicle’s mission plan (Patr´on et al. 2008c). The aim
is to provide with a mission plan as close as possible to the
original. In such case, plan optimality during the adaptation
process can be sacrified for plan stability (Fox et al. 2006).
This level of control can also benefit from the use of
knowledge frameworks. Adaptation is limited to the quality
and scope of the available information. Therefore, to adapt
mission plans due to unforeseen and incipient faults, it is
required that accurate information is available, to recognize
that a fault has occurred and deduce the root cause of the
failure.
Current robotic solutions are generally equipped with
Failure Diagnosis and Detection (FDD) systems based on
damage control that results in the vehicle resurfacing in
the event of any fault in the system. But future operations
will require long-endurance unattended systems that operate
even while being partially damaged or while having constrained
capabilities. Hence, it is important to develop system
which not only detect failure in the component but also
provide mission plan modules capable of adapting and recovering
from these failures. This exchange of meaningful
and reliable knowledge between FDDs and mission planners
can leverage on the use of semantic knowledge frameworks.
Once the knowledge has been transferred from the FDD,
the adaptation approach combines robust execution and mission
plan repair in order to maximize system performance
and response time. Two levels of repair are possible. If the
fault coming from the FDD is incipient or non-critical, a repair
at the executive level is performed. This entails adapting
the intances and parameters of the action performing the
task without changing the action itself. If a critical fault on
a component is diagnosed, a repair at the plan level is performed
that changes the action requiring of the component
and the constraints on the action. During this decision making
process, a combination of refinement and unrefinement
phases of the constraints of a partial ordered mission plan
can be used (Patr´on et al. 2008b) (see Fig. 10).
Single platform
Current mission plan solutions for underwater platforms
are procedural and static (Hagen 2001). If behaviors are
added (Pang et al. 2003), they are only to cope with possible
changes that are known a-priori by the operator. In order
to achieve higher levels of autonomy, plan adaptability
should be available not only to procedural waypoint-based
approaches on trajectory planning but also to a declarative
goal-based solution for adaptive mission planning.
On a goal-oriented command and control, decision tools
can be developed providing support for adaptive data sampling,
optimization of resources and fault recovery capabilities
of the platform (Bellingham, Kirkwood, and Rajan
2006).
The potential benefits of the planning capabilities for
adaptive data sampling have been studied by Rajan (Rajan et
al. 2007) and Fox (Fox et al. 2007). An example of this application
is the T-Rex system, an adaptive planning architecture
with notions of timing and onboard resource management
responsiveness to opportunistic science events (Mc-
Gann et al. 2007).
In a similar way, fault-tolerant mission planning adaptation
can also contribute towards providing more mission
flexibility and robustness. When active and passive measures
fail to protect the vehicle, or unexpected hardware failures
occur, the focus of the mission should shift to ’reconfigure’
itself to use alternative combinations of the remaining
resources (see Fig. 9). In the underwater environment, autonomous
embedded recoverability, is a key capability for
vehicle’s endurance. This can be achieved via adaptation of
the vehicle’s mission plan (Patr´on et al. 2008c). The aim
is to provide with a mission plan as close as possible to the
original. In such case, plan optimality during the adaptation
process can be sacrified for plan stability (Fox et al. 2006).
This level of control can also benefit from the use of
knowledge frameworks. Adaptation is limited to the quality
and scope of the available information. Therefore, to adapt
mission plans due to unforeseen and incipient faults, it is
required that accurate information is available, to recognize
that a fault has occurred and deduce the root cause of the
failure.
Current robotic solutions are generally equipped with
Failure Diagnosis and Detection (FDD) systems based on
damage control that results in the vehicle resurfacing in
the event of any fault in the system. But future operations
will require long-endurance unattended systems that operate
even while being partially damaged or while having constrained
capabilities. Hence, it is important to develop system
which not only detect failure in the component but also
provide mission plan modules capable of adapting and recovering
from these failures. This exchange of meaningful
and reliable knowledge between FDDs and mission planners
can leverage on the use of semantic knowledge frameworks.
Once the knowledge has been transferred from the FDD,
the adaptation approach combines robust execution and mission
plan repair in order to maximize system performance
and response time. Two levels of repair are possible. If the
fault coming from the FDD is incipient or non-critical, a repair
at the executive level is performed. This entails adapting
the intances and parameters of the action performing the
task without changing the action itself. If a critical fault on
a component is diagnosed, a repair at the plan level is performed
that changes the action requiring of the component
and the constraints on the action. During this decision making
process, a combination of refinement and unrefinement
phases of the constraints of a partial ordered mission plan
can be used (Patr´on et al. 2008b) (see Fig. 10).
Obstacle avoidance
Obstacle avoidance
Once the navigation and mapping error has been bounded,
an adaptation of the trajectory and waypoints is required
during mission when unexpected events on the planned trajectory
are sensed. Collision avoidance and escape is a key
capability for underwater vehicle navigation.
Traditional approaches to collision avoidance and escape
are purely reactive and usually involve an algorithmic formulation
of response dictated by the objects geometries and
locations and the relative location of the vehicle. Recent approaches
in rules of collision (Benjamin et al. 2006) and
obstacle avoidance and escape scenarios (Evans et al. 2007)
have focused on reducing susceptibility by looking at the
adaptation of the vehicle’s trajectory plan. These deliberative
behaviours provide the defined and bounded event responses
that link to geometric methods of trajectory planning.
At the trajectory planning level, lifelong planning incremental
search methods have been found to decrease computation
time while still locating the shortest trajectory between
vehicle and goal (Koenig, Likhachev, and Furcy
2004). Instead of starting from scratch every time a trajectory
must be adapted, they reuse data found in previous
searches to save on computation time. Wave propagation
techniques can also provide with smoother and shorter trajectories
than classic discrete search approaches. This fast
marching methods are capable of dealing with uncertainty,
dynamic objects and kinematics of the vehicle during the
adaptation process (Pˆetr`es et al. 2007). A real application of
these techniques in the underwater environment is shown in
Fig. 8.
Once the navigation and mapping error has been bounded,
an adaptation of the trajectory and waypoints is required
during mission when unexpected events on the planned trajectory
are sensed. Collision avoidance and escape is a key
capability for underwater vehicle navigation.
Traditional approaches to collision avoidance and escape
are purely reactive and usually involve an algorithmic formulation
of response dictated by the objects geometries and
locations and the relative location of the vehicle. Recent approaches
in rules of collision (Benjamin et al. 2006) and
obstacle avoidance and escape scenarios (Evans et al. 2007)
have focused on reducing susceptibility by looking at the
adaptation of the vehicle’s trajectory plan. These deliberative
behaviours provide the defined and bounded event responses
that link to geometric methods of trajectory planning.
At the trajectory planning level, lifelong planning incremental
search methods have been found to decrease computation
time while still locating the shortest trajectory between
vehicle and goal (Koenig, Likhachev, and Furcy
2004). Instead of starting from scratch every time a trajectory
must be adapted, they reuse data found in previous
searches to save on computation time. Wave propagation
techniques can also provide with smoother and shorter trajectories
than classic discrete search approaches. This fast
marching methods are capable of dealing with uncertainty,
dynamic objects and kinematics of the vehicle during the
adaptation process (Pˆetr`es et al. 2007). A real application of
these techniques in the underwater environment is shown in
Fig. 8.
Trajectory planning
Trajectory planning
Navigation and mapping
Existing geographical information for trajectory planning
can be represented using knowledge-based frameworks.
However, mapping of the environment depends on the uncertainty
in the position of the vehicle when the obstacles
are observed and mapped. Navigation in unknown underwater
environments entails high levels of uncertainty.
Geolocating a vehicle on theWorld’s surface can be accurately
performed by using Global Positioning System (GPS)
receivers. But GPS does not work underwater as the radio
signals on which it depends cannot pass through water. Dead
reckoning is the standard technique used underwater and involves
use of Inertial Measurement Units (IMUs), a combination
of accelerometers and gyroscopes. Abbe error, magnification
of angular error over distance, can be detrimental
to dead reckoning. Inertial Navigation Systems (INS) integrate
accelerations and rates to provide a Kalman filtered
navigation solution. They use inputs from acoustic transmitters
and receivers to pin-point the platform’s location. However,
the range of these systems is limited to no more than a
few nautical miles thus restricting the vehicles autonomy. A
better approach involves aiding the INS with a Doppler Velocity
Log (DVL) sensor that measures the displacement rate
over the seabed. No matter how accurate the sensors are, the
errors in these systems grow with time and the platform becomes
progressively lost unless it is able to obtain external
references from acoustic devices or from a GPS receiver on
the surface. This uncertainty in the vehicle’s location gets
passed to the geolocation of the observed elements in the
environement during the mapping process.
A promising alternative is Simultaneous Localisation And
Mapping (SLAM). Using SLAM a platform maps the environment
and uses the map to localise itself in it. The map can
be georeferenced if the platform maintains an estimate of its
absolute position when the process of mapping is started.
A recent development has shown that it is possible to postprocess
and smooth the SLAM solution using the Rauch-
Tung-Striebel smoother (see Fig.7). This new technique,
coined SLAM-RTS, has been used to create better maps of
the environment and to help the trajectory planning systems
to accurately locate the sensed obstacles in the map (Patr´on
and Tena-Ruiz 2006).
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