ArticlePDF Available

Experiments with Deliberative Planning on Autonomous Underwater Vehicles


Abstract and Figures

We describe the process of improving the onboard autonomy of LAUV-Seacon AUVs with an in-situ planning agent. Deliberative planning is achieved by extending the existing control architecture with T-REX and a domain model description which is specific to the Seacon AUVs and typical mission scenarios. We discuss the required architectural changes as well as results and lessons learned. The field deployments showed that operators were able to command the vehicles by requesting high-level objectives like locations to be visited and areas to survey both before and during mission execution instead of sending low-level waypoint or actuator commands. The commanded plans, generated onboard according to the domain model are inherently safe, abstracted and easier to deal with for a non-technical operator.
Content may be subject to copyright.
Experiments with Deliberative Planning on
Autonomous Underwater Vehicles
e Pinto and Jo˜
ao Sousa
Faculdade de Engenharia da Universidade do Porto
Underwater Systems and Technology Lab
Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Email: {zepinto, jtasso}
eric Py and Kanna Rajan
Monterey Bay Aquarium Research Institute
Moss Landing, California, United States
Email: {fpy, kanna.rajan}
Abstract—We describe the process of improving the onboard
autonomy of LAUV-Seacon AUVs with an in-situ planning agent.
Deliberative planning is achieved by extending the existing
control architecture with T-REX and a domain model descrip-
tion which is specific to the Seacon AUVs and typical mission
scenarios. We discuss the required architectural changes as well
as results and lessons learned. The field deployments showed that
operators were able to command the vehicles by requesting high-
level objectives like locations to be visited and areas to survey
both before and during mission execution instead of sending low-
level waypoint or actuator commands. The commanded plans,
generated onboard according to the domain model are inherently
safe, abstracted and easier to deal with for a non-technical
The undersea realm, where AUVs tipically operate, is
highly unknown and dynamic. However, most AUVs operate
by strictly following a pre-defined plan, created previously
by a human operator at the surface. In order to counteract
events like strong currents, obstacles or any other failures, the
plan script must articulate a number of fallback mechanisms
together with specific mission objectives. The result of having
humans defining complex plans can result in error-prone
definitions of the intended vehicle behavior which can only be
verified through techniques like model checking or computer
simulations prior to mission execution.
Instead of creating the plans beforehand, researchers at the
Monterey Bay Aquarium Research Institute (MBARI) have
designed, tested and fielded the Teleo-Reactive EXecutive
(T-REX) [1], [2], [3], [4], [5], an Open Source plan execution
framework that uses temporal Constraint-based Planning [6]
with plans synthesized onboard the AUV, both before and
while executing the mission autonomously and according to
a set of high-level objectives. The generated plans are, by
definition, a valid sequence of actions according to a given
domain model, which enforces the safety of the resulting
Leveraging that work, LSTS (the Underwater Systems and
Technology Lab, Univ. of Porto) has recently extended their
toolchain in order to optionally encompass an onboard plan-
ner/executive based on T-REX and NASA’s EUROPA planner.
T-REX is a mission executive that allows different reactors
(threads of execution) to interact with other reactors by provid-
T-REX Agent
Iridium interface
Mission Manager
Legend :
Interface reactor
Planning reactor
Goal flow
Observation flow
Fig. 1. An example of a T-REX agent.
ing internal (controlled) timelines and using external timelines,
controlled by other reactors’ Fig. 1 shows an instance of
interacting reactors within one T-REX agent. T-REX has
been developed aiming the integration of deliberative planning
reactors, allowing each reactor to have a different look-ahead
(how far they can plan) and latency (maximum amount of time
to produce a plan). T-REX timelines hold tokens that have a
type, starting time and duration (possibly indefinite) together
with other attributes. Reactors interact by adding observation
tokens into internal timelines and posting goals to external
timelines. Goals are similar to observations but their start time
is in the future. If they eventually get done, they are posted
as an observation in the timeline they were requested. Details
of T-REX are beyond the scope of this work.
In this paper we describe the required steps towards inte-
grating T-REX into the existing software toolchain in Section
III, field test experiments in Section IV, an evaluation of the
results in Section V and, finally some conclusions in Section
VI. First we provide the overall context and objectives of the
exercise in Section II.
The Rapid Environmental Picture (REP) exercise is an
annual event resulting from a collaboration between Porto
University and the Portuguese Navy. The main objective of
Fig. 2. Location of REP-12 off the coast of Portugal.
this exercise is to advance and test operational concepts and
underwater technologies with supporting assets from the Navy,
focusing on underwater mine warfare.
The REP-12 exercise from the 9th to the 20th July 2012,
took place off the coast of Sesimbra (Fig. 2 – A) and in an
underwater archeological site next to Tr´
oia peninsula (Fig.
2 – B), with numerous partners with differing goals. As a
result, there were a multitude of tests concerning ocean floor
mapping, acoustic communications, delay-tolerant networking,
UAV-AUV communications and deliberative planning. More
details about the REP-12 , including tests and results are
available in [7].
A. The LSTS Toolchain
LSTS has been developing different types of unmanned
vehicles including AUVs, UAVs, ASVs and ROVs. The objec-
tive of the lab is to create networked vehicle systems where
vehicles can be both endpoints of communication and routers
of information. To achieve this, LSTS has created a modular
software toolchain [8] that is agnostic of vehicle and com-
munication means allowing for mixed-initiative cooperative
behavior of vehicles and humans.
This toolchain is divided into three major components:
DUNE ,IMC and Neptus which are briefly described next.
1) DUNE :The DUNE Unified Navigation Environment
is a POSIX-compliant onboard software that uses a black-
board mechanism for inter-process communication. DUNE
tasks (processes) send IMC (described next) messages to a
shared bus from which they receive messages of subscribed
types. Different DUNE configurations (tasks and parameters)
are used to support different vehicles and execution profiles
like simulation, hardware and HWIL simulation.
The onboard software tasks are divided into a set of control
layers, from the low-level sensor/actuation tasks up to the plan
and team supervisors that command maneuver execution. An
overview of these control layers can be seen in Fig. 5.
Goto Area
Survey Popup
maneuverDone? maneuverDone?
Fig. 3. A Neptus scripted plan example.
Fig. 4. The LSTS toolchain.
2) IMC :The Inter-Module Communicatons protocol is a
message-oriented protocol that is independent of the underly-
ing communication mean (UDP, HTTP, TCP, acoustic modem
or GSM). Its specification is documented in XML from which
different language bindings are generated automatically [9].
The IMC communication protocol is used all across the
toolchain (Fig. 4), both between operator consoles and ve-
hicles (network nodes) and between DUNE tasks. DUNE tasks
communicate with each other locally via an IMC message bus
and in a distributed system using IMC messages transported
over available communication means.
3) Neptus: The Neptus Command and Control software
runs on the operator console, serving as the graphical interface
between humans and a robotic network. This software supports
different mission phases: planning, simulation, execution, data
revision and dissemination [10], [11].
For Neptus, a plan is defined as a finite state machine.
Each state is tied to a maneuver with specific parameters and,
between states, different transition conditions can be specified.
In each state, the vehicle executes a maneuver that changes its
physical state (through motion) and in the mean time a set of
events can be flagged during execution (triggering transitions).
Examples of maneuvers are “Goto”, “Loiter”, “FollowTra-
jectory” and “Rows”. These take specific parameters like
destination, speed or depth. Transition conditions are currently
a limited set of events like “ManeuverDone” or “Limits-
Breached” as shown in Fig. 3.
These 3 components have been used together numerous
times to control multiple unmanned vehicles having one or
more operators in the loop for different purposes [12], [13].
This is one of the most extensive toolchains we are aware of
in regular operational use.
Plan interface
Vehicle Interface
Maneuver interface
Platform interface
Plan supervisor
Loiter controller
Goto controller StationKeeping controller
Navigation Guidance
IMU driver CTD driver Thruster driver Fin servos driver
Vehicle Supervisor
Team Supervisor
Plan commands Plan state
Vehicle commands Vehicle state
Maneuver commands Maneuver state
Actuator commands Sensors state
Guidance commands Navigation state
Fig. 5. Control layers of the LSTS toolchain.
In order to enforce safety of the vehicle’s behavior, the
toolchain provides the possibility of setting per-vehicle safety
limits like maximum depth, minimum bottom distance, speed
and area bounds among others. On board the vehicle, a
special supervisor task continually monitors the vehicle’s state
and validates it according to these limits, flagging the event
“LimitsBreached” in case the state is not valid. This approach
doesn’t prevent sending of erroneous plans but has been
otherwise an effective and reactive approach to vehicle safety
since it usually triggers execution of fail-safe behavior.
B. Operational Requirements
In order to come up with good deliberative planning prim-
itives, we divised a set of operational requirements of the
resulting software architecture that we now enumerate.
1) Operators should be able to follow vehicle execution (as
they do in scripted plans).
2) All generated plans should be validated according to its
safety before execution.
3) The safety limits should (still) be continuously verified
while the vehicle is executing any kind of autonomous
4) The operator should be able to pause deliberative plan-
ning and command execution of scripted plans, resuming
deliberative planning afterwards.
5) Pausing of deliberative planning should be possible with
the vehicle underwater by using an acoustic modem.
6) The high-level objectives should abstract vehicle specific
paramaters so that those are handled by the onboard
C. Integrated Architecture
Integration of deliberative planning into the LSTS toolchain
was undertaken by having T-REX running as a separate
process inside the vehicles. This T-REX instance contains a
set of deliberative reactors, based on the EUROPA planner and
a platform-specific reactor.
The deliberative reactors use a domain model (described in
Section III-F) that allows them to interface with the Platform
TREX plugin
TrexGoal / TrexCommand
TREX task
Message bus TREX timelines
IMC (localhost)
Fig. 6. Components of the integrated system.
specific reactor. This reactor connects to DUNE through IMC
and translates incoming messages into T-REX observations
which are posted to an internal timeline. The Platform reactor
also accepts T-REX goals which are eventually translated into
DUNE commands (sent through IMC).
In order to maintain the feature of being able to command
the vehicle via scripted plans, T-REX can be temporarily
deactivated on user request. While T-REX is deactivated, it
still is doing state synchronization but the domain model states
that no commands can be sent to the vehicle while deactivated.
This allows deliberative reactors to receive any new goals and
plan future actions to take when T-REX is reactivated on user
D. Safety Mechanisms
Several safety mechanisms are enforced by this new archi-
tecture. The supervisor that continually monitors breaching of
operational limits remains active. This is possible since the
interface from T-REX with the onboard software resides in
the planning control layer and, as such, all the underlying
mechanisms can still be active. In the event the vehicle goes
outside these limits, T-REX control is deactivated and the
previously existing fail-safe mechanism (or a contingency
maneuver) is executed.
When the limits are breached, a “Blocked” observation is
generated. Since the planner’s domain model requires an “Ac-
tive” observation in order to generate any kind of commands,
T-REX will only send new plans after being reactivated by
user request (sending of a specific IMC message). Moreover,
the same behavior is activated if the vehicle receives an
Abort” command either via Wi-Fi or acoustic modem while
At each tick, all T-REX reactors are required to do a
synchronization step which consists of integrating observations
from other reactors into their state. If a reactor is not able to
produce its internal state based on the received observations
(for instance if observations generate an impossible state), it
gets terminated by T-REX . In our architecture we react to
this event by having an aditional reactor (Safety Bug) that
simply listens to the timelines of all other reactors and if they
get terminated, it will generate an “Abort” command which
results in fail-safe behavior and T-REX being temporarilly
On top of the mentioned safety mechanisms, the domain
model includes information about the operational limits (as a
posted observation) on the vehicle and the planner is required
to create plans which are safe, meaning that they wont drive
the vehicle outside of these limits. As a result, if there are any
objectives which require the vehicle to exit the operational
limits, they are adequately rejected by the planner.
E. T-REX reactors
The T-REX implementation involved the creation of differ-
ent reactors for encapsulating functionalities and thus improve
flexibility. This flexibility comes from the possibility of swap-
ping reactors with others with similar interfaces in terms of
used timelines. Fig. 7 depicts the resulting set of reactors and
their interactions through timelines. We next describe briefly
how they function.
1) Platform: This reactor receives IMC messages from
DUNE and generates corresponding observations in its con-
trolled timelines. Moreover, this reactor also accepts maneuver
goals which eventually result in the request and execution of
plans in the vehicle.
2) Goal Queue: This reactor receives a goal description
(XML) and posts it into the corresponding timeline. The
received goals can come from DUNE or any other process
capable of writing into a Unix pipe.
3) Safety Bug: This simple reactor listens to all timelines
and if it detects that the owner has terminated, sends an
Abort” command to DUNE .
4) Navigator: This is a deliberative reactor that accepts
At” goals. As a result it will post maneuver goals that will
drive the vehicle towards the desired locations.
5) Surface: This is a deliberative reactor that drivers the
vehicle towards the surface periodically by posting objectives
to the Navigator reactor.
F. Domain Model
The deliberative reactors (Navigator and Surface) require a
domain model with a set of rules that describe how the world
evolves according to the observations. In order to control the
world, deliberative reactors post goals that will eventually be-
come observations. The domain model for the LAUV-Seacon
is specified in NDDL (Novel Domain Description Language)
and interpreted by the EUROPA planner. This is currently a
simple model focusing on maneuvers and their impact on the
state of the world.
Listing 1 shows a snippet of the domain model used by
the Navigator deliberative reactor during the exercise on the
Seacon. The first rule (on the estimator timeline) determines
that, for a vehicle to arrive at a location, the arrival must
be met by a maneuver whose destination matches that of the
desired location. The second (platform timeline) enforces that
Estimator : : At {
me t b y ( P l a t f o r m . M an e uv er ma n eu v er ) ;
man eu ve r . l a t = = l a t ;
m an e uv e r . l o n == l o n ;
m an e uv e r . d e p t h == d e p t h ;
m an eu v er . s e c s == s e c s ;
m an eu ve r . t o l e r a n c e == t o l e r a n c e ;
P l a t f o r m : : Ma n eu ve r {
s t a r t s d u r i n g ( O pL im i ts . L i m i ts l i m i t s ) ;
s p e e d <= l i m i t s . m ax Spe ed ;
l i m i t s . m in Sp ee d <= s p e e d ;
i n s i d e ( l a t , l on , d ep t h , l i m i t s ) ;
s t a r t s d u r i n g ( T r e x S u p e r v i s i o n . A c ti v e a c t i v e ) ;
me t b y ( I d l e i d l e ) ;
m ee t s ( E s t i m a t e d S t a t e . P o s i t i o n p ) ;
d i s t == w g s 8 4 d i s t ( l a t , l o n , p . l a t , p . l o n ) ;
dist <= t o l e r a n c e ;
Listing 1. NDDL fragment relating At and Maneuver predicates
command state
Safety Bug
Goal Queue
Fig. 7. T-REX reactors in the periodic surfacing model.
the maneuver starts only if T-REX is currently able to send
commands (activated), tests if the maneuver is safe and that
it terminates near the desired location (otherwise a new plan
must be found).
Safety of the maneuver is checked against the currently
known operational limits by limiting the speed of the vehicle
and checking that the position is safe. The entire domain model
is available [14].
In order to improve the operator’s situational awareness and
reduce navigational uncertainty, a second deliberative reactor
was added that enforces the vehicle to come to the surface and
acquire a GPS signal periodically. As a result, the ammount
of time that the vehicle stays underwater is bounded by a sur-
facing time parameter, part of a “PeriodicSurface” predicate.
The resulting set of reactors and timelines is depicted in Fig.
In REP-12 the Seacon vehicle (see table I) was used in
initial tests as an ASV and subsequently in the nominal AUV
Fig. 8. LSTS Seacon vehicle in AUV mode (left) and in ASV mode
(right) used in the REP-12 exercise.
The ASV mode is achieved by mounting two floaters and
adding a long-range Wi-Fi access point and antenna. The idea
behind this modification is that it allows the vehicle to be used
as a mobile communications gateway between surface (Wi-Fi,
GSM) and underwater (acoustic modem). Both configurations
of this vehicle are shown in Fig. 8.
A. Operator Interface
For these tests, a simple operator interface was developed
as a Neptus map interaction plugin. From this interface, the
operator can add locations to be visited (goals) by clicking
the map. The sent goals, visible in the map as circular
shape, can be recalled by the user similarly or they can be
(eventually) marked as done. Moreover, the interface also
allows deactivating / activating of T-REX.
Since the interface is based on Neptus it is possible to
monitor the vehicle’s execution while adding new goals or
create scripted plans. Scripted plans can be interleaved with
T-REX execution by temporarily disabling T-REX and order
execution of a previously sent plan.
B. Simple Model
The first experiment consisted in testing a simple domain
model that has no initial objectives and accepts locations to be
visited as goals. The Navigator reactor, receives these goals
and generates goals in other’s reactors timelines that ultimately
result in one or more maneuvers to be executed by the vehicle.
The objective of this test was to measure CPU usage
while stress-testing the system. This was done by adding and
Length 140 cm
Diameter 16 cm
Maximum depth 50 m
Endurance 8 hours at 3 knots
Communications Wi-Fi, Acoustic Modem, HSDPA/GSM
Actuation 4 directional fins and 1 brushless motor
Navigation GPS (surface), LBL, AHRS
Optional payloads (AUV) CTD, sidescan sonar, multibeam
Optional payloads (ASV) Long-range Wi-Fi antenna, video camera
recalling several objectives, blocking T-REX with pending
objectives, interrupting maneuver execution manually, etc. All
of the previous requires the planner to do replanning in order
to try to conteract unpredicted occurences.
C. Periodic Visits
This test, made with Seacon in AUV mode, consisted
in slight modification of the previous (simple) model. We
changed the model by enforcing the AUV to visit two points,
at the surface alternately whenever more than 2 minutes have
passed since the previous visit. In the mean time, the vehicle
is able to visit any other locations desired (received as goals
from the user).
D. Volume Survey
In this model, a deliberative reactor (VolumeSurvey) was
added to the simple model. This reactor takes goals that are
parametrized by a set of 4 points and the number of legs
(number of long transects). As a result, VolumeSurvey will
command a survey of the area (quad resulting from connecting
the 4 points) by generating visit-location goals which are
handled by the Navigator reactor.
E. Periodic Surfacing
In this test, we changed the first (simple) model by enforcing
the vehicle to acquire a valid GPS fix strictly periodically. This
was modelled by stating (in NDDL) that the AUV may not
be underwater for more than 5 minutes and if an underwater
maneuver will take longer than the time of the next surface,
then it will be interrupted for the vehicle to come to the
The integrated system was tested both with AUVs and
ASVs successfully and it was used together with the standard
scripted planning (interleaved). The system was stress-tested
by blocking / reactivating T-REX several times, adding and
recalling goals and also by posting unsafe goals. All of the
former requires the planner to replan its actions.
For the surface model, the resulting behavior depitcted in
Fig. 9, shows that the vehicle did one or more maneuvers
(different depths) while underwater and has actively come to
the surface whenever there were no objectives left underwater.
Moreover, around 10:52, the vehicle took more than 5 minutes
for completing the maneuver and, as a result, it popped up at
the surface to acquire GPS and then dived again to continue
the maneuver, as expected.
Overall, the system allowed controlling of the vehicle in
a more intuitive fashion. Instead of directly controlling the
vehicle, the operator simply states what needs to be done.
The resulting execution, however, was unpredictable in the
sense that the vehicle may chose any sequence of actions that
fulfil the objectives and this sequence of actions, despite safe,
is not known apriori. For instance, considering that surfacing
is a dangerous maneuver hen there is boat traffic, with the
Periodic Surfacing model it was impossible to know exactly
Fig. 9. Depth and number of GPS satellites in the periodic surface
when and where the vehicle was going to surface (only that
it wouldn’t stay underwater for more than 5 minutes).
T-REX was successfully integrated into the LSTS
toolchain and tested with AUVs and ASVs. The planning
interface was intuitive enough for non-trained operators to
command and recall goals using the plugin developed for the
Neptus graphical interface.
We will change the architecture in order to eliminate the
need of maneuver preemption (stops between maneuvers). On
the other hand, we aim to signal maneuver completion but
switch to other maneuver only when there is other maneuver
ready to be executed.
The tested models, while still simple, showed that it is
possible to use this control architecture to command the LSTS
vehicles. We plan to improve the models by adding new
reactors, provide better context awareness and augment with
more complex behaviors. We also plan to extend Neptus with
a shore-side planner that is able to command several vehicles
by interacting with a set of distributed T-REX timelines.
The authors would like to thank the Portuguese Navy for
providing excellent conditions in the testing of this work at
sea in Sesimbra, during the REP-12 exercise.
The research leading to these results has received funding
from the European Commission FP7-ICT Cognitive Systems,
Interaction, and Robotics under the contract #270180 (NOP-
TILUS). MBARI authors are funded from a block grant from
the Packard Foundation and and in part by NSF grant #IIS-
1127975 and NOAA grant #NA11NOS4780055.
[1] C. McGann, F. Py, K. Rajan, H. Thomas, R. Henthorn, and R. McEwen,
“A Deliberative Architecture for AUV Control,” in Intnl. Conf. on
Robotics and Automation (ICRA), Pasadena, May 2008.
[2] C. McGann, F. Py, K. Rajan, J. P. Ryan, and R. Henthorn, “Adaptive
Control for Autonomous Underwater Vehicles,” in AAAI, Chicago, IL,
[3] C. McGann, F. Py, K. Rajan, J. P. Ryan, H. Thomas, R. Henthorn, and
R. McEwen, “Preliminary Results for Model-Based Adaptive Control of
an Autonomous Underwater Vehicle,” in Intnl. Symp. on Experimental
Robotics (ISER), Athens, 2008.
[4] F. Py, K. Rajan, and C. McGann, “A Systematic Agent Framework
for Situated Autonomous Systems,” in 9th International Conf. on Au-
tonomous Agents and Multiagent Systems, Toronto, Canada, May 2010.
[5] K. Rajan and F. Py, “T-REX: Partitioned Inference for AUV Mission
Control,” in Further Advances in Unmanned Marine Vehicles, G. N.
Roberts and R. Sutton, Eds. The Institution of Engineering and
Technology (IET), 2012.
[6] M. Ghallab and D. Nau and P. Traverso, Automated Planning Theory
and Practice. Elsevier Science, 2004.
[7] “Rep12 experiment web site.” [Online]. Available:
[8] J. Pinto, P. Calado, J. Braga, P. Dias, R. Martins, and E. Marques, “Im-
plementation of a control architecture for networked vehicle systems,
in IFAC Workshop on Navigation, Guidance and Control of Underwater
Vehicles (NGCUV2012), 2012.
[9] R. Martins, P. Dias, E. Marques, J. Pinto, J. Sousa, and F. Pereira,
“Imc: A communication protocol for networked vehicles and sensors,
in OCEANS 2009 - EUROPE, may 2009, pp. 1 –6.
[10] P. Dias, G. Gonc¸alves, R. Gomes, J. Sousa, J. Pinto, and F. Pereira,
“Mission planning and specification in the neptus framework,” in
Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE
International Conference on, may 2006, pp. 3220 –3225.
[11] J. Pinto, P. S. Dias, R. Gonc¸alves, E. Marques, G. M. Gonc¸alves, J. a. B.
Sousa, and F. L. Pereira, “NEPTUS – A Framework to Support the
Mission Life Cycle,” in 7th IFAC Conferent on Manoeuvring and Control
of Marine Craft, Lisbon, Portugal, 2006.
[12] A. Tinka, S. Diemer, L. Madureira, E. Marques, J. B. Sousa, R. Martins,
J. Pinto, J. E. da Silva, P. Saint-Pierre, and A. M. Bayen, “Viability-based
computation of spatially constrained minimum time trajectories for an
autonomous underwater vehicle: implementation and experiments.” in
American Control Conference, St. Louis, Missouri, USA, 2009.
[13] R. Martins and J. Sousa, “Concepts and tools for coordination and
control of networked ocean-going vehicles,” in AUV 2010, Monterey,
California, USA, 2010.
[14] “Trex2-agent source code repository on google code.” [Online].

Supplementary resource (1)

... The field of automated planning has contributed significantly to advanced fields like Space exploration (Rabideau et al., 1999). The first AI planning algorithm, STRIPS, was developed in 1971 at Stanford University to solve a basic planning problem for a mobile robot, and since then the field has progressed significantly and contributed to applications such as spaceexploration (Bernard et al., 1999), inspection (Streich and Adria, 2004), prevention of disasters, rescue operations and operations with robots and autonomous systems (Pinto et al., 2012;Xue and Lekkas, 2020;Hinostroza and Lekkas, 2022). In addition to STRIPS, other fundamental AI planning algorithms are hierarchical task network (HTN) (Erol et al., 1994) and GraphPlan (Blum and Langford, 1999). ...
Offshore oil and gas industry has a strong incentive to improve its traditional operations and move towards more remote controlled and automated installations. This allows for improved efficiency, reduced cost and improved quality, and safety by removing personnel out of harm’s way. The use of Unmanned Ground Vehicles (UGVs) in these upcoming platforms, is relevant for Inspection and Maintenance (I&M) operations. Traditionally, UGVs are used only for pre-defined tasks and have no capabilities for replanning, if a new task is required or any unexpected event occurs. This paper presents a novel concept for I&M operations using automated planning for UGVs. The automated planner is based on a temporal planning algorithm, and considers actions related to, for example, visiting a specific waypoint, inspect a sensor or manipulate an actuator. Also, the proposed system allows to perform replanning in case of any specific location needs to be revisited or a path is blocked. In addition, we couple the mission planner with a UGV guidance, navigation and control system, which has path planning, path following and control capabilities. To assess the performance of the proposed system, an use case for I&M operations on board of an oil and gas platform was simulated and promising results were obtained.
... AI planning, also called automated planning and scheduling, is a sub-area of artificial intelligence (AI) that studies the deliberation process in a computational way to be applicable to intelligent agents and robots (Ghallab et al., 2004). The first AI planning algorithm, STRIPS, was developed in 1971 at Stanford University (Fikes and Nilsson, 1971) to solve a basic planning problem for a mobile robot, and since then the field has progressed significantly and contributed to applications such as space-exploration (Bernard et al., 1999), prevention of disasters (Nau et al., 1999), rescue operations (Currie and Tate, 1991) and operations with robots and autonomous systems (Pinto et al., 2012;Xue and Lekkas, 2020). In addition to STRIPS, other fundamental AI planning algorithms are hierarchical task network (HTN) (Erol et al., 1994) and GraphPlan (Blum and Langford, 1999). ...
Maritime autonomous surface ships (MASS) have become a topic of intensive research in academia and industry, with focus areas such as path following and tracking, low-level control, collision avoidance, situational awareness, and others. However, high-level mission planning has received less attention in the literature, although it constitutes an important aspect of autonomy. This paper takes a step towards closing this gap by developing a basic mission planning system, which can generate a feasible and efficient sequence of high-level actions (plans) that are subsequently executed by the control system. The mission planner is based on the Graphplan algorithm, and considers actions related to, for example, entering docking mode, visiting a specific location, or starting (un)loading of containers, which are then to be executed by appropriate control systems. In addition, we couple the mission planner with the ship's guidance, navigation and control system, which has path planning, path following and fuzzy logic-based collision avoidance capabilities. To assess the performance of the proposed method, we present a set of simulations of a MASS navigating in a realistic marine environment including static obstacles and other ships.
... For more dynamic behaviors such as onboard automated planning, DUNE provides a back-seat driver API. This API is implemented as a maneuver (called FollowReference) whose desired path is defined elsewhere in the network (communicated via IMC) [5]. Whenever the FollowReference is activated, DUNE goes into an "external control" mode that tracks the reference (setpoint) provided by its external controller. ...
Conference Paper
Full-text available
This paper presents recent software developments which allow the coordination of multiple AUVs over high-latency and low-bandwidth satellite communications. These developments span over multiple components of the LSTS toolchain: protocols, onboard plan execution and adaptation, satellite communications and temporal planning. The developments have been recently put to test in the middle of the Pacific Ocean where a fleet composed of 7 AUVs, 3 UAVs and 3 ASVs was used to map the SubTropical Front while operated from two base stations: one onboard the R/V Falkor oceanographic ship and another at Portugal mainland.
... The next step was to allow the vehicles to adapt its behavior onboard so that, in case of failure, it would replan its actions to fulfil provided objectives. This was done by integrating the T-REX plan deliberation and execution framework into the LSTS toolchain [6]. At this stage the user simply provided each vehicle with a series of objectives (areas to survey, points to visit) and constraints (maximum time between popups at surface to communicate with base station over Iridium). ...
... • Adding more expressiveness to the language, e.g., extensions for supporting vehicle "teams" and associated dynamics (Shahir et al. (2012); Sousa et al. (2004)). • Further integration with the LSTS toolchain, including the development of a Neptus NVL plugin, and the use of NVL as a backend language for deliberative planning Pinto et al. (2012Pinto et al. ( , 2013a. • Formal analysis of a NVL programs, in order to establish a priori guarantees on program execution. ...
Full-text available
The use of unmanned vehicle networks for diverse applications is becoming widespread. It is generally hard to program unmanned vehicle networks as a "whole", however. The coordination of multiple vehicles requires careful planning through intricate human intervention, and a high degree of informality is implied in what concerns the specification of a "network program" for an application scenario. In this context, we have been developing a programming language for expressing global specifications of coordinated behavior in unmanned vehicle networks, the Networked Vehicles' Language (NVL). In this paper we illustrate the use of the language for a thermal pollution plume tracking scenario employing unmanned underwater vehicles. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
... The toolchain also includes an autonomy plugin, T-REX, easing the programming of new objectives and increasing mission execution safety. T-REX has been tested on AUVs and USVs [7], and on UAVs [8]. ...
Conference Paper
Full-text available
This paper presents a demonstration conducted in the Trondheim fjord, where an unmanned surface vehicle (USV) developed and operated by Maritime Robotics AS was used to relay acoustic information sent by a REMUS 100 autonomous underwater vehicle (AUV), to an onshore operation center. The AUVs mission was to conduct a survey of a Short Sunderland type aircraft, which was sunk just outside Munkholmen in 1945. The progress of the AUV and the USV were available in real-time throughout the demonstration, and was presented in Maritime Robotics' in-house Vehicle Control Station (VCS) software. The acoustic telemetry data was also relayed to the REMUS Vehicle Interface Program (VIP), used for monitoring the health of the AUV. Furthermore, the USV used the telemetry messages sent by the AUV, containing e.g. position and heading, to autonomously track the AUV during the survey. Throughout the mission, only 63% of the modem messages was received by the USV, and the longest time between telemetry updates was 6 minutes, but the USVs ability to track the AUV and relay data was successfully demonstrated.
Full-text available
In this chapter Teleo-Reactive Executive (T-REX) is designed, developed, tested and deployed as an onboard adaptive control system that integrates artificial intelligence (AI)-based planning and probabilistic state estimation in a hybrid executive. Probabilistic state estimation integrates a number of science observations to produce a likelihood that the vehicle sensors perceive a feature of interest. Onboard planning and execution enable adaptation of navigation and instrument control based on the probability of having detected such a phenomenon. It further enables goal-directed commanding within the context of projected mission state and allows for replanning for off-nominal situations and opportunistic science events.
Full-text available
This paper describes the layered control architecture and its software implementation developed and used at the Underwater Systems and Technology Laboratory. The architecture is implemented as a toolchain which consists on three main entities: DUNE onboard software, Neptus command and control software and a common IMC message-based communication protocol. The LSTS software toolchain has been tested throughout various field deployments where it was used to control heterogeneous autonomous vehicles like AUVs, ASVs, UAVs and ROVs in both single and multi-vehicle operations.
Full-text available
The Neptus distributed command and control framework for operations with vehicles, sensors, and human operators in inter-operated networks is presented. This is done in the context of applications, technologies, and field tests. There are applications for world representation and modeling, mission planning, simulation, execution control and supervision, and post-mission analysis. This is done in a mixed initiative fashion allowing the intervention by experienced human operators. XML abstract data types and XSLT technologies facilitate vehicle-interoperability and the standardization of interactions. A publish/subscribe (P/S) middleware framework for communications in a distributed environment enables the transparent inter-operability of communication networks. A console builder together with the P/S middleware allow the user to configure operating consoles for different vehicles. Results from field tests validate the overall framework and provide directions for future work. Copyright © 2006 IFAC
Conference Paper
Full-text available
This paper presents the Inter-Module Communication (IMC) protocol, a message-oriented protocol designed and implemented in the Underwater Systems and Technology Laboratory (LSTS) to build interconnected systems of vehicles, sensors and human operators that are able to pursue common goals cooperatively by exchanging real-time information about the environment and updated objectives. IMC abstracts hardware and communication heterogeniety by providing a shared set of messages that can be serialized and transferred over different means. The described protocol contrasts with other existing application level protocols by not imposing or assuming a specific software architecture for client applications. Native support can be automatically generated for different programming languages and/or computer architectures resulting in optimized code which can be used both for networked nodes and also for inter-process and inter-thread communication. The protocol has already been tested throughout various experiments led by LSTS where it has taken care of communications between vehicles, sensors and operator consoles. We are now developing the protocol in the direction of having multi-vehicle cooperation using live data from environmental sensors and mixed-initiative user interaction.
Conference Paper
Full-text available
A viability algorithm is developed to compute the constrained minimum time function for general dynamical systems. The algorithm is instantiated for a specific dynamics (Dubin's vehicle forced by a flow field) in order to numerically solve the minimum time problem. With the specific dynamics considered, the framework of hybrid systems enables us to solve the problem efficiently. The algorithm is implemented in C using epigraphical techniques to reduce the dimension of the problem. The feasibility of this optimal trajectory algorithm is tested in an experiment with a light autonomous underwater vehicle (LAUV) system. The hydrodynamics of the LAUV are analyzed in order to develop a low-dimension vehicle model. Deployment results from experiments performed in the Sacramento River in California are presented, which show good performance of the algorithm.
Conference Paper
Full-text available
Autonomous Underwater Vehicles (AUVs) are an increasingly important tool for oceanographic research demonstrating their capabilities to sample the water column in depths far beyond what humans are capable of visiting, and doing so routinely and cost-effectively. However, control of these platforms to date has relied on fixed sequences for execution of pre-planned actions limiting their effectiveness for measuring dynamic and episodic ocean phenomenon. In this paper we present an agent architecture developed to overcome this limitation through on-board planning using Constraint- based Reasoning. Preliminary versions of the architecture have been integrated and tested in simulation and at sea.
Conference Paper
Full-text available
We describe a novel integration of Planning with Probabilistic State Estimation and Execution. The resulting system is a unified representational and computational framework based on declarative models and constraint- based temporal plans. The work is motivated by the need to explore the oceans more cost-effectively through the use of Autonomous Underwater Vehicles (AUV), requiring them to be goal-directed, perceptive, adaptive and robust in the context of dynamic and uncertain conditions. The novelty of our approach is in integrating deliberation and reaction over different temporal and functional scopes within a single model, and in breaking new ground in oceanography by allowing for precise sampling within a feature of interest using an autonomous robot. The system is general-purpose and adaptable to other ocean going and terrestrial platforms.
Conference Paper
Full-text available
We present a formal framework of an autonomous agent as a collection of coordinated control loops, with a recurring sense, plan, act cycle. Our framework manages the informa- tion ow within the partitioned structure to ensure consis- tency in order to direct the ow of goals and observations in a timely manner. The resulting control structure improves scalability since many details of each controller can be encap- sulated within a single control loop. This partitioned agent design promises a domain-independent, scalable and robust approach for control of real-world autonomous robots oper- ating in dynamic environments. We validate our framework with experimental results from deployments in two dierent real-world domains.
An abstract characterization of the networked vehicle systems under study is presented together with the corresponding modeling, coordination and control challenges. This is done with the help of an example of 24/7 persistent operations. The novel aspects of these systems are emphasized and the nature of the new challenges is discussed. This is done with reference to fundamental concepts in control and computation, namely state and control spaces, control strategies, optimization, behavior equivalence, process, interaction and composition. The discussion is illustrated with tools and technologies from the Underwater Systems and Technologies Laboratory from Porto University.