485 research outputs found
Semantic-based adaptive mission planning for unmanned underwater vehicles
Current underwater robotic platforms rely upon waypoint-based scripted missions which
are described by the operator a-priori. This renders systems incapable of reacting to
the unexpected. In this thesis, we claim that the ability to autonomously adapt the
decision making process is the key to facilitating the change over from human intervention
to intelligent autonomy. We identify goal-based declarative mission planning
as an attractive solution to autonomous adaptability because it combines autonomous
decision making with higher levels of human interaction.
Goal-based mission planning requires the use of abstract knowledge representation
and situation awareness to link the prior knowledge provided by the operator with
the information coming from the processed sensor data. To achieve this, we propose
a semantic-based knowledge representation framework that allows this integration of
prior and processed information among all different agents available in the platform.
In order to evaluate adaptive mission planning techniques, we also introduce a novel
metric which measures the proximity between plans. We demonstrate that this metric
is better informed than previous metrics for measuring the adaptation process.
In this thesis we implement three different approaches to goal-based mission planning
in order to investigate which approach is most appropriate under different circumstances.
The first approach, continuous mission planning, focusses on long-term
deployment. This approach is based on a continuous re-assessment of the status of
the mission environment. Using our proximity metric, we evaluated this approach
and show that there is a high degree of similarity between our approach and the humanly
driven adaptation, both in a known static environment and in a partially-known
dynamic discoverable environment. The second, service-oriented mission planning,
makes use of the semantic framework to provide autonomous mission planning for
the dynamic discovery of the services published by the different agents in the system.
This allows platform independence, easing the manual creation of mission plans, and
robustness to changes. We show that this approach produces the same plans as the
baseline which was explicitly provided with the platform configuration. The last approach,
mission plan repair, handles the scenario where small changes occur in the
mission environment and there are limited resources for planning. We develop and
deploy a mission plan repair approach within a semantic-based autonomous planning
system in a real underwater vehicle. Experiments demonstrate that the integrated system
is capable of providing mission adaptation for maintaining the operability of the
host platform in the face of unexpected events
An energy-aware architecture : a practical implementation for autonomous underwater vehicles
Energy awareness, fault tolerance and performance estimation are important aspects for
extending the autonomy levels of today’s autonomous vehicles. Those are related to the
concepts of survivability and reliability, two important factors that often limit the trust
of end users in conducting large-scale deployments of such vehicles. With the aim of
preparing the way for persistent autonomous operations this work focuses its efforts on
investigating those effects on underwater vehicles capable of long-term missions.
A novel energy-aware architecture for autonomous underwater vehicles (AUVs) is
presented. This, by monitoring at runtime the vehicle’s energy usage, is capable of
detecting and mitigating failures in the propulsion subsystem, one of the most common
sources of mission-time problems. Furthermore it estimates the vehicle’s performance
when operating in unknown environments and in the presence of external disturbances.
These capabilities are a great contribution for reducing the operational uncertainty that
most underwater platforms face during their deployment. Using knowledge collected while
conducting real missions the proposed architecture allows the optimisation of on-board
resource usage. This improves the vehicle’s effectiveness when operating in unknown
stochastic scenarios or when facing the problem of resource scarcity.
The architecture has been implemented on a real vehicle, Nessie AUV, used for real sea
experiments as part of multiple research projects. These gave the opportunity of evaluating
the improvements of the proposed system when considering more complex autonomous
tasks. Together with Nessie AUV, the commercial platform IVER3 AUV has been involved
in the evaluating the feasibility of this approach. Results and operational experience,
gathered both in real sea scenarios and in controlled environment experiments, are
discussed in detail showing the benefits and the operational constraints of the introduced
architecture, alongside suggestions for future research directions
The challenges and opportunities of artificial intelligence in implementing trustworthy robotics and autonomous systems
Effective Robots and Autonomous Systems (RAS) must be trustworthy. Trust is essential in designing autonomous and semi-autonomous technologies, because “No trust, no use”. RAS should provide high quality of services, with the four key properties that make it trust, i.e. they must be (i) robust for any health issues, (ii) safe for any matters in their surrounding environments, (iii) secure for any threats from cyber spaces, and (iv) trusted for human-machine interaction. We have thoroughly analysed the challenges in implementing the trustworthy RAS in respects of the four properties, and addressed the power of AI in improving the trustworthiness of RAS. While we put our eyes on the benefits that AI brings to human, we should realise the potential risks that could be caused by AI. The new concept of human-centred AI will be the core in implementing the trustworthy RAS. This review could provide a brief reference for the research on AI for trustworthy RAS
Service-oriented agent architecture for autonomous maritime vehicles
Advanced ocean systems are increasing their capabilities and the degree of autonomy more and more in order to perform more sophisticated maritime missions. Remotely operated vehicles are no longer cost-effective since they are limited by economic support costs, and the presence and skills of the human operator. Alternatively, autonomous surface and underwater vehicles have the potential to operate with greatly reduced overhead costs and level of operator intervention. This Thesis proposes an Intelligent Control Architecture (ICA) to enable multiple collaborating marine vehicles to autonomously carry out underwater intervention missions. The ICA is generic in nature but aimed at a case study where a marine surface craft and an underwater vehicle are required to work cooperatively. They are capable of cooperating autonomously towards the execution of complex activities since they have different but complementary capabilities. The architectural foundation to achieve the ICA lays on the flexibility of service-oriented computing and agent technology. An ontological database captures the operator skills, platform capabilities and, changes in the environment. The information captured, stored as knowledge, enables reasoning agents to plan missions based on the current situation. The ICA implementation is verified in simulation, and validated in trials by means of a team of autonomous marine robots. This Thesis also presents architectural details and evaluation scenarios of the ICA, results of simulations and trials from different maritime operations, and future research directions
AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE
The work in this thesis concerns with the development of a novel multisensor data fusion
(MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic
and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous
underwater vehicle (AUV) navigation system, formed by an integration of global positioning
system and inertial navigation system (GPS/INS).
The Kalman filter has been a popular method for integrating the data produced
by the GPS and INS to provide optimal estimates of AUVs position and attitude. In
this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is
proposed. The former is used to fuse the data from a variety of INS sensors whose
output is used as an input to the later where integration with GPS data takes place.
The use of an adaptation scheme based on fuzzy logic approaches to cope with the
divergence problem caused by the insufficiently known a priori filter statistics is also
explored. The choice of fuzzy membership functions for the adaptation scheme is first
carried out using a heuristic approach. Single objective and multiobjective genetic
algorithm techniques are then used to optimize the parameters of the membership
functions with respect to a certain performance criteria in order to improve the overall
accuracy of the integrated navigation system. Results are presented that show
that the proposed algorithms can provide a significant improvement in the overall
navigation performance of an autonomous underwater vehicle navigation.
The proposed technique is known to be the first method used in relation to AUV
navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd.,
Qinetiq, Subsea 7 and South West Water PL
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