Improving the Coordination Efficiency of Multiple AUV Operations
Using Prediction of Intent
Chris C. Sotzing, David M. Lane
Ocean Systems Laboratory, Heriot-Watt University
Edinburgh, Scotland, UK, EH14 4AS
http://osl.eps.hw.ac.uk/
ccs1@hw.ac.uk D.M.Lane@hw.ac.uk
Abstract
This research addresses the problem of coordinating multiple autonomous underwater
vehicle (AUV) operations. A mission executive has been created that uses multi-agent
technology to control and coordinate multiple AUVs in communication deficient
environments. By incorporating a simple broadcast communication system in conjunction
with real time vehicle prediction this architecture can handle the limitations inherent in
underwater operations and intelligently control multiple vehicles. In this research efficiency
is evaluated and then compared to the current state of the art in multiple AUV control.
Keywords : Autonomous Underwater Vehicles, Distributed Control, Multi-Robot Systems,
Robot
Coordination,
MCM,
Agent
Prediction,
Hybrid
Architectures
Introduction
The goal of this research is to merge cooperative robotics, Autonomous Underwater Vehicle
(AUV) and multi-agent technologies together to create a robust multi-AUV control
architecture. 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.
Despite these benefits, current architectures for such operations are often simple extensions
of single vehicle designs that do not allow for large amounts of communication, let alone
cooperation. Ideally, a multi-agent architecture should allow for a significant increase in
mission efficiency without requiring a proportional increase in communication. This study
utilises the architecture created in the Ocean Systems Laboratory (Figure 1) and creates an
intelligent mission executive (the DELPHÍS system) which is compared to the current state of
the art in multiple-AUV control.
16th International Symposium on Unmanned Untethered Submersible Technology.
University of New Hampshire. 23-26 August 2009.
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Figure 1: OSL tri-level hybrid control architecture with this research in red.
In architectures such as these missions are planned in the deliberative layer using an adaptive
mission planning module. These plans are then passed to the executive layer where they are
completed by the mission executive. When faced with problems or new information the
mission executive traditionally requires the mission planner to perform a mission re-plan.
This process is time consuming and not practical in most real world situations.
Aimed to work in conjunction with the mission planner described in [1], this work presents
an intelligent mission executive that can coordinate vehicles in communication poor
environments while keeping the need for a mission re-plan to a minimum. This executive
repair functionality aims to keep most conflicts local (solvable by the mission executive) and
not global (only solvable with a mission re-plan).
Background and Related Work
Multi-Agent Systems
A multi-agent system is a computational system where two or more software modules or
components, known as agents, work together to solve a problem. An agent is considered to
be a locus of problem solving activity that tries to fulfil a set of goals in a complex, dynamic
environment [2]. Depending on the type of environment, an agent can take many different
forms. Robots can be considered agents that can sense and actuate in the physical world.
The ability of such systems to exchange information, decisions and plans at distance and on
heterogeneous platforms make them a powerful tool.
Cooperative Robotics
Research into cooperative robotics dates back to the late 1980’s when the challenges of
multiple robot systems began to be investigated. This was the fusion of two different
research areas, singular robotics and distributed problem solving, which until that point had
been independent. The work can be grouped into two general areas: non-mobile robots (for
example, production line robotic assembly), and the less constrained, arguably more
challenging area of mobile robots.
Most of the initial research in the area of multiple Autonomous Mobile Robots (AMR),
concentrated on the behaviour-based paradigm [3]. The biological inspiration for this
paradigm has led many researchers into investigating techniques that can mimic the social
characteristics of insects and animals. However much of this innovative research has to date
focused on robots working in highly constrained environments [4].
Many of the real-world applications being undertaken and proposed for AMRs are highly
task and plan oriented. Examples of applications include exploration/mapping [5], area patrol
[6] and formation control [7]. Existing architectures draw strongly on conventional computer
structures and symbolic algorithms (for example, script-based task execution, and expert
knowledge systems); it is not clear that biologically inspired behaviour-based paradigms will
be useful in the foreseeable future.
The challenge is that as the role of AMRs continues to expand, and as the level of autonomy
increases, how can autonomous decision making be extended across multiple vehicles?
AUV Control Architectures
The current state of the art in AUV control consists of three different types of architectures:
deliberative, reactive and hybrid [8]. Each of these control paradigms are widely used in the
mobile robotics world though the descriptions here will be focused specifically on their
application in AUVs.
Deliberative architectures are based around planning. This planning process utilises a world
model that consists of a set of goals. A plan is generated based on these goals and then
executed by a control system. One of the downsides of deliberative architectures is that all
actions have to be planned by the high level planner modules. This becomes a problem when
a plan has to be modified during execution.
Reactive architectures, also known as behavioural architectures, were the catalyst that helped
start the field of collaborative robotics. These architectures are event based systems where
certain situations result in certain behaviours. The real world acts as a model to which the
robot reacts [8]. Though reactive architectures solve the quick reaction time problem present
in deliberative architectures, they lack their global planning ability and consequently aren’t as
able to solve complex problems without large populations of robots.
Hybrid architectures combine the benefits of deliberative and reactive architectures into one.
Normally these systems are divided into three distinct layers: the deliberative high level layer
for planning, the executive layer for vehicle instruction and sensor processing, and the
functional reactive layer for data input and motor control [9]. Because hybrid architectures
implement characteristics from deliberative and reactive architectures, they are currently the
most common choice for AUV control [10].
Multi-AUV Coordination
Current AUV missions are getting more complex and it is becoming common to require
multiple vehicles to work together to solve these types of tasks. Currently there are two main
approaches towards getting AUVs to cooperate: the adaptation of single vehicle architectures,
and the application of current cooperative robotics architectures.
Currently, the state of the art in AUV mission control is script-based mission execution
systems. Missions are written in the form of scripts which are executed sequentially by the
vehicle. In these systems task order is predefined by the user, though event based scriptswitching is possible [11]. The benefit of such systems is that because all vehicle actions are
predefined, behaviour is very predictable. Conversely, though predictable, scripted plans
don’t allow for dynamic task allocation and consequently often do not optimally execute the
mission given unforeseen events.
The most common way to coordinate multiple AUVs is what is known as a stoplight system.
In these systems current single-AUV script execution systems are modified to include
stoplight points where vehicles can synchronise with each other. The benefit of such systems
is that current AUV control architectures can be utilised with few modifications. However,
autonomy is sacrificed because the user has to plan the static sequence of the mission in
advance including all synchronisation points.
Another approach to multi-AUV cooperation is the modification of existing cooperative
robotics approaches to the underwater domain. Sariel et al. [12] proposes a bidding system
used to coordinate simulated AUVs in a mine-countermeasures operation. In this system any
vehicle can act as auctioneer to generate bids on a task, and assuming good communication,
allows for dynamic goal selection and a high level of cooperation. However, the dependency
on consistent communication illustrates a major challenge when applying existing multi-robot
techniques to the underwater domain: how can a multi-AUV architecture maintain a high
level of cooperation and simultaneously minimise the need for an similarly high level of
communication?
System Design
The DELPHÍS system [13] is an intelligent mission execution system that allows for efficient
multi-AUV coordination in environments where communication cannot be guaranteed. The
system acts as the mission executive in a standard hybrid three-layer architecture (Figure 1).
The sub-architecture for the DELPHÍS system is shown in Figure 2.
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Figure 2: System schematic of the DELPHÍS system.
The Abstract Layer Interface (ALI) developed in the Ocean Systems Laboratory uses a
defined set of messages that are translated to be understood by the host architecture. In using
the ALI this system can be used to control any vehicle simply by applying its generic
constructs as the mission executive of the host-vehicle architecture. Within the DELPHÍS
system there are three major modules: the mission model, AUV database and mission
controller.
Mission Model
The mission model module contains the mission file and controls interaction with it,
including status updates, goal retrieval, etc. A custom mission representation system was
created that embraces the limitations of underwater operations in addition to allowing for
intelligent multi-agent, multi-vehicle coordination. The system, known as BIIMAPS
(Blackboard Integrated Implicit Multi-Agent Planning Strategy), is described in more detail
below.
Mission plans are currently represented in a number of different ways. The most common
options are if-then-else scripts and hierarchical task networks. If-then-else scripts are the
current state of the art however they tend to be rather inflexible and unable to cope with
unforeseen events. Hierarchical task networks show more promise for flexibility due to the
fact that the goals aren’t necessarily constrained by any order. The BIIMAPS system is based
on these plan representations and adds the functionality of blackboard systems to allow for
more intelligent, robust behaviour.
In the BIIMAPS system (Figure 3), mission plans are represented as a tree of tasks, or goals,
which are the major building blocks of the system plan. Goals (represented by large circles
in Figure 3) can either be atomic (leaf goal), or divided into sub-goals; each goal can be in one
of three states: complete, ready or locked. In addition, goals can be marked current when
they are currently being executed by an agent.
Floating
Goal
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Figure 3: BIIMAPS mission modelling system diagram. Large circles represent goals; white circles
contain operations. Dependency is represented by a dotted arrow.
All leaf goals have operations associated with them (represented by white circles in the
diagram). An operation is the behaviour associated with the certain goal. For instance if a
goal is to navigate to a certain point, the operation would be the waypoint coordinates. In
addition non-leaf goals can also have operations associated with them. If a goal’s sub-goals
were a set of waypoints the root goal’s operation could be to operate a camera so that it
recorded throughout the execution of all sub-goals. This situation is represented in Figure 3.
To determine when a goal has been completed, each goal has a condition. Conditions can be
simply the completion of a goal’s operation or they can be externally dependent, such as a
message received from another agent. The conditions for non-leaf goals can be a logical
combination of their sub-goals, as shown in the diagram.
Goals can also have dependencies and constraints applied to them to determine when they are
available for execution. Dependencies are when a goal relies upon another (represented by
the dotted arrow in Figure 3). A good example of this would be if goal x is to navigate to a
point, goal y might be to clear the area of mines. Goal x would then depend upon goal y. A
constraint is similar, but instead of referring to another goal, it refers to the state of the world.
The BIIMAPS system was designed as a mission representation system with blackboard
functionality. In this case BIIMAPS itself can be considered the blackboard and the agents
updating the mission act as the knowledge sources. Because of this the system can be rolled
back to a previous state should any previous update that other updates depend upon become
untrue.
AUV Database
The AUV database is a module that contains information about all of the AUVs in the
mission, a system similar to that being used in [12]. This information includes static data
such as vehicle type and dimensions as well as variable data like battery life, average speed,
sensors, and sensor status. Variable data is updated periodically to ensure that each AUV has
the most recent status of its peers.
The goal of the AUV database is to allow for intelligent goal selection as well as behaviour
prediction in the case of a loss of communications. When communications are lost, each
vehicle still has to make decisions about the mission, despite not being able to contact the
others. By consulting the AUV data-base, predictions can be made using the most recent
status of each vehicle.
Mission Controller
The mission controller is the main decision making unit in the architecture. Using data from
the mission model and the AUV database it coordinates all execution level decision making
within the vehicle. It is made up of a number of sub-modules that fall into three main
categories: communication, goal selection and agent prediction.
Figure 4: RAUVER autonomous underwater vehicle (Ocean Systems laboratory, Heriot-Watt
University).
Communication in the mission controller consists of both internal and external message
passing. Internal communication utilises the OceanSHELL broadcast message system [14].
In this way control commands can be easily passed to the autopilot in addition to allowing for
simple interfacing with any OceanSHELL capable vehicle, including the hover capable AUV,
RAUVER (Figure 4).
This architecture utilises an acoustic broadcast system whereby vehicles periodically send out
status information to all other vehicles in range. This information includes AUV data with a
unique ID number, AUV type (hover capable, torpedo, etc.), current position, average speed,
battery life as well as mission information, consisting of current goal, a list of previously
achieved goals and newly discovered targets.
Broadcast communication has been chosen over vehicle to vehicle (unicast) transmissions to
allow for greater ability to handle intermittent communications, which is common in systems
operating in the underwater environment.
System Functionality
In DELPHÍS system each AUV is considered an agent and functions as such. Initially, all
AUVs start out with an identical copy of the mission plan. Before beginning execution of the
mission, the status beacon is started so that vehicles acting in the system can register with
each other, subsequently populating each other’s AUV data-bases. This allows for external
AUV information (including position, intention, etc.) to be factored in to initial AUV goal
selection.
Goal selection works in conjunction with the mission model to select the best possible goal
for the AUV to achieve. Utilising the BIIMAPS system, the mission model is able to return a
list of only the goals in the mission that are available for execution based on the conditions,
dependencies and status of each goal. This list is then passed to the goal selection algorithm
where it is pruned down to a single goal representing the best available task for the given
AUV. If no goal is available the AUV will wait in a holding pattern until one becomes
available. Once the goal is executed, its status is updated in the mission model and the loop
repeats until the mission is complete. The algorithm is represented in pseudocode below.
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When AUV broadcasts are received, the in-formation pertaining to the sending AUV is used
to update the receiving AUV database. In addition, the current goal and list of completed
goals from the sending AUV are passed into the receiving mission model, synchronising it.
In this way all mission models contain the same data across all vehicles.
Information in the broadcast relating to the mission status is continually built upon
throughout runtime. As the vehicles run the mission their list of completed goals (and
discovered targets) continually grows. Due to this, every message received contains the
entire mission history of the sending vehicle. This is extremely important for two reasons:
first it allows for simple reconciliation of mission data in the likely event of dropped
communications, and second, it allows for new AUVs to easily enter the mission at any point
during mission execution.
Prediction
The ability to handle and synchronise data in intermittent communications is not enough for a
multi-AUV coordination system, since in many cases vehicles may be out of contact for
extended periods of time. In these scenarios it is important to be able to predict the actions of
other AUVs to enable more intelligent goal selection.
This architecture handles this eventuality with an AUV-Monitor module that keeps track of
the AUV database and makes predictions for vehicles when the time since last
communication rises above a certain threshold. Utilising the most recent data from the AUV
database (last known position, average speed, current goal, etc.) the AUV-Monitor can
estimate the current position of the vehicle. In addition it can update the mission model
should the predicted position indicate that the vehicle has achieved its goal (in this case the
updated status is recorded in the mission model with a predicted flag). Additionally, the
AUV-Monitor can then calculate the most likely next goal by passing the AUV information
into its own goal selection algorithm. When communication returns, the mission model is resynchronised and the predicted values replaced with actual data.
As multiple vehicles are working together and constantly updating a common plan, in certain
cases the current goal for a certain AUV may become less suitable than another during
execution. Rather than force complete execution on any goal attempted, this architecture has
a Mission-Monitor module that keeps track of the mission model and constantly checks to see
if the current goal is the best possible choice. Should it be determined that this is not the
case, the current goal execution is stopped and goal selection is run again. In this way the
system ensures that each vehicle is always performing the best possible action.
In the cases of intermittent communications, it sometimes occurs that two AUVs are found to
be executing the same goal. The Mission-Monitor is able to detect this and calculate which
AUV is most suited for execution. Because this module is running on both vehicles and each
one has information about the other in their AUV database, they will come to the same
decision.
Results
In order to evaluate the system’s ability to increase efficiency, this architecture has been
tested against the current state of the art in multiple-AUV operations (script-based stop-light
systems) in executing a mine countermeasures (MCM) mission.
Figure 5: Three RAUVER AUVs coordinating an MCM mission in simulation using the augmented
reality visualisation tool found in [15].
In this scenario, efficiency of multiple AUV operations is defined as a combination of the
following:

Mission speed

Goal redundancy

Missed goals

Mine discovery
Experiments were conducted in simulation to determine the efficiency of the system in
deteriorating communication environments. A high-fidelity simulator developed in the
Ocean Systems Laboratory can replace and very accurately model the RAUVER AUV. This
in combination with an augmented reality visualisation tool [15] provides a realistic
environment for the software-in-the-loop evaluation of vehicle execution code. It also
provides a useful test-bed for the development and testing of vehicle modules before
embedding them on the vehicle. An image of the DELPHÍS system coordinating a simulated
MCM mission is shown in Figure 5.
Efficiency
To evaluate efficiency this research compared four different multi-AUV control architectures:
a stoplight system and three versions of the DELPHÍS system (optimised, un-optimised and
prediction failure). The un-optimised system was simply the DELPHÍS system with all
optimisation modules disabled. The prediction failure system was the DELPHÍS system with
the prediction module predicting actions happening faster than in reality.
The MCM mission was executed by each system for 2-4 vehicles in varying degrees of
communication success ranging from 100-10%. Results can be seen in Figure 6, Figure 7 and
Figure 8.
MCM - 2 AUVs - Efficiency
120.00
100.00
Average Efficiency
80.00
Optimized
Unoptimized
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Prediction Failure
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Figure 6: Efficiency data for 2 AUVs.
MCM - 3 AUVs - Efficiency
120.00
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Average Efficiency
80.00
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Unoptimized
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Prediction Failure
60.00
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Figure 7: Efficiency data for 3 AUVs.
MCM - 4 AUVs - Efficiency
120.00
100.00
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80.00
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Unoptimized
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Prediction Failure
60.00
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Figure 8: Efficiency data for 4 AUVs.
As can been seen here the efficiency of the four coordination systems shows clear trends as
the communication rate drops. The optimised DELPHÍS system is able to maintain a high
level of efficiency throughout, with only a minor drop towards the 10-20% communication
success rate. The prediction failure DELPHÍS system was only marginally less efficient
reporting data that was just under that of the optimised system. This is logical as incorrect
predictions would result in some decrease in efficiency. However, in many cases the other
mission optimisation techniques would make up for any major errors.
The un-optimised DELPHÍS system did not fare as well as communication rates dropped. It
showed a clear decline in all three group sizes. This decline is due to the amount of
coordination conflicts that occurred while communications were down. More specifically, it
was the lack of ability to fix these conflicts when communications returned that affected
efficiency.
The stoplight system also showed lower efficiency than the optimised DELPHÍS system,
however this was mostly due not to coordination errors (since stoplight systems are by
definition scripted a priori) but to the time it took for the mission to be accomplished. The
stoplight system was often slower than the other three systems and this had a major effect on
efficiency. A good example of this can be seen in the 2 vehicle data where the stoplight
system recorded significantly lower efficiency than the other systems. This was because in
the 2 vehicle scenario, the MCM mission took significantly longer when controlled by the
stoplight system than it did when controlled by the 3 DELPHÍS systems.
Stoplight efficiency data wasn’t only affected by time however. There were some
coordination errors as well mostly, regarding the newly discovered mines which were not
initially scripted in the mission but added at runtime. These targets were chosen as they came
available however in low communications and without the mission optimisation techniques
of the DELPHÍS system any conflicts couldn’t be worked out. This was particularly common
in the experiments with 3 and 4 vehicles.
In some cases efficiency of the runs was returned as above 100%. In this research mission
time was compared to the expected data which is the time data returned by the DELPHÍS
system at 100% communications. In the cases where efficiency was above 100% this simply
meant that the mission was accomplished faster than the DELPHÍS system. This is evident in
the 4 vehicle data (Figure 8) where the stoplight system starts off more efficient than the
others.
Behaviour
A major advantage of the DELPHÍS system is that it does not require the mission to be
started with all the vehicles at once. As mentioned before, AUVs can be added during run
time and this has been tested and proven to work with four vehicles (and the data indicates it
working with many more). This capability has proven to be extremely useful especially
when a mission is taking longer than expected.
Interesting emergent behaviour has been discovered in the case of two vehicles attempting
the same goal (this situation has to be manually created, as it only occurs when
communication is down and prediction has failed). In this case sometimes AUVs will
alternate as to which vehicle should execute the goal, until an agreement is made. This seems
to be due to the time between status broadcasts and only happens when vehicles are nearly
equidistant from the goal. When the time between broadcasts is decreased this behaviour
disappears.
Prediction is an extremely important ability for a truly robust multi-AUV architecture. These
results have shown that the efficiency of multi-AUV coordination systems with prediction is
higher than those systems that lack it. In addition, even with inaccurate prediction, the
DELPHÍS system was able to maintain a higher level of efficiency than the current state of
the art.
The DELPHÍS systems ability to handle and communicate newly discovered targets has been
shown to work in most conditions except complete communication loss. When a target is
discovered it is added to the local mission model with an ID unique to the AUV that found it.
This is done so that if while out of communication range two AUVs discover the same target,
they aren’t given the same ID number. Due to this, even in badly degraded communications
vehicles can accurately transmit new targets to other vehicles without conflict. When no
communication is possible, targets are simply held by the AUV and transmitted when
communication returns.
Conclusions
This system for coordinating multiple AUVs has shown promising results in increasing
mission efficiency as compared to the current state of the art. Through the use of dynamic
goal selection, distributed mission plans and agent prediction the DELPHÍS system can
successfully coordinate multiple heterogeneous vehicles in most ranges of communication.
Though focused on AUVs this work can easily be extended to any UxV platform.
Next Steps
Recent work has applied these results to real vehicles and validated these findings.
[16] presents results showing co-ordination efficiency and the benefits of predicting intent for
mine countermeasures search-classify-map mission using a REMUS 100 and the HWU
Nessie III hover capable AUV. Open water trials were conducted in Loch Earn, Scotland.
Having already successfully controlled a single vehicle (RAUVER) mission the results
presented here were reflected in multiple AUVs in the field.
In the future, a further, important aspect of this research that will be studied further is the
interaction between this system and the mission planner [1]. Because it was the DELPHÍS
system being tested, this study focused on its ability to coordinate vehicles and simplified the
problem by not requiring mission re-plan. In the future this interaction between architecture
levels will be investigated.
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Acknowledgements
The work reported in this paper was funded by the Systems Engineering for Autonomous Systems
(SEAS) Defence Technology Centre established by the UK Ministry of Defence. The authors thank
the members of the Ocean Systems Laboratory at Heriot-Watt University and SeeByte Ltd. for their
generous support.