Intelligent Modelling, Control and Navigation for AUVs.
Colloq. on Autonomous Underwater Vehicles and their Systems
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The demand for long mission duration, associated reliability and availability requirements, operation in unbinign, unstructured environments, real time operating requirements, and flexibility of use has led to increasing levels of autonomy in underwater vehicles. Autonomy in above water vehicles is quite old, with modern cruise missiles exemplifying extreme performance capability in navigation and control in complex environments under sever counter measure attack! Remotely piloted (air) vehicles for surveillance for civil and military usage are both sophisticated and common place today, but offer little intelligence and autonomy in that the majority are teleoperated or at most under significant levels of supervised control/management, with the majority of processing being carried out at the command and control centre. Whilst this approach minimisers the cost of onboard processing, it demands very high communication bandwidth, and lack of flexibility and robustness. It has been demonstrated that decentralised-distributed systems architectures potentially offer a wide range of quality attributes such as ease of systems integration, interoperability, scaling, portability and modularity, inherent robustness and survivability etc, that are necessary for effective implementation of AUVs. Such an architecture must be applicable to all the subsystems necessary for constructing AUVs ie. vehicle locatistaion, obstacle detection, vehicle navigation (and guidance), vehicle control, vehicle local planning and replanning and resource management, and vehicle mission tasking. For the purposes of this talk we will focus on the problems associated with vehicle state determination and control - since they are critical to all other AUV requirements and are relatively well understood and researched. To achieve improved quality of estimation, robustness and fault tolerance, multiple disparate sensors are increasingly being utilised in AUVs, so that following a particular sensor failure other sensors can cover the loss and at worst provide graceful degrading system performance. This in turn requires a mechanism for fusing data from disparate data sources (including symbolic and linguistic) and associated estimation algorithms for dealing with uncertainty and ignorance of coverage etc. Aspects of multi-sensor data fusion for vehicle localisation and obstacle detection, together with developments in estimation algorithms will be covered in the presentation. The generation of full envelope control laws for AUVs that accommodate mass temperature, salinity, depth, etc changes that in turn minimises fuel usage and is robust to damage etc, is extremely difficult by conventional controller design methods. Usually, these methods require an exchange between performance and robustness. In this paper we illustrate AUV modelling and control which enables both criteria to be satisfied utilising Neurofuzzy methods and online data gathering via the sensor bed. The talk will be illustrated by a selection of videos illustrating land based intelligent autonomous vehicles.
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