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Title: Neurofuzzy multi-sensor data fusion for helicopter obstacle avoidance
Author: Doyle, Rory Stephen
ISNI:       0000 0001 3432 0190
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 1997
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Hazardous weather conditions significantly limit the operational capability of civil helicopters. This limitation arises from the crew's inability to determine the location of obstacles in the environment by sight. In order to assist the crew in these circumstances a range of equipment and sensors may be installed in the helicopter. However, with multiple sensors on board, the problem of efficiently assimilating the large amount of imagery and data available generates a significant workload. A reduction of the workload may be achieved by the automation of this assimilation (sensor fusion) and the design of a system to guide the pilot along obstacle free paths. In order to provide the guidance to avoid obstacles a system must have knowledge about the obstacles' possible positions and likely future positions relative the system's own aircraft. Since the information being provided by the sensors will not be perfect, (i.e. it will have some uncertainty associated with it), and since the process model, which must be used to predict any future positions, will also be uncertain, the required positions must be estimated. As the dynamics of moving obstacles will be a priori unknown, it will be necessary to learn process models for them. The dynamics of the obstacles cannot be guaranteed to be linear, therefore these process models must be capable of reflecting this non-linear behaviour. The uncertain information produced by the various sensors will be related to required knowledge about the obstacles by a sensor model, however this relationship need not be linear, and may even have to be learned. Currently used estimation techniques (e.g. the ordinary extended Kalman filter) are inadequate for estimating the uncertainty involved in the obstacles' positions for the highly non-linear processes under consideration. Neural network approaches to non-linear estimation have recently allowed process and sensor models to be learned (sometimes implicitly), however these approaches have been quite ad hoc in their implementation and have been even more negligent in the estimation of uncertainty. The main contributions of this research are the design of non-linear estimators which may use process and sensor models that result from learning processes, and the use of the output of these estimators to determine guidance for obstacle free paths through the environment in 3 dimensions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Robotics