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Title: Cerebellum inspired robotic gaze control
Author: Lenz, Alexander
ISNI:       0000 0004 2722 3300
Awarding Body: University of the West of England, Bristol
Current Institution: University of the West of England, Bristol
Date of Award: 2011
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The primary aims of this research were to gain insight into control architectures of the mammalian brain and to explore how such architectures can be transferred into real-world robotic systems. Specifically, the work presented in this thesis focuses on the cerebellum, a part of the brain implicated in motor learning. Based on biologically grounded assumptions of uniformity of the cerebellar structure, one specific (but representative) example of cerebellar motor control was investigated: the mammalian vestibula-ocular reflex (VOR). During movement, animals are faced with disturbances with respect to their vision system. The VOR compensates for head motion by driving the eyes in the opposite direction of the head and thereby stabilising the image on the retina. Due to severe delays in the visual feedback signal, the VOR is required to operate as an open-loop controller, which uses proprioceptive information about head motion to instigate eye movements. As a feed-forward control system, it requires calibration to gradually learn the required motor commands. This is achieved by the cerebellum through the utilisation of the delayed visual information encoding image slip. In order to explore the suitability of a recurrent cerebellar model to achieve similar performance in a robotic context, engineering equivalents of the biological sub-systems were developed and integrated as a distributed embedded computing infrastructure. These included systems for rotation sensing, vision, actuation, stimulation and monitoring. Real-time implementations of cerebellar models were developed and then tested on two custom designed robotic eyes: one actuated with electrical motors and the other operated by pneumatic artificial muscles. It is argued that the successful transfer of cerebellar models into robotic systems implicitly validates these models by providing an existence proof in terms of structure, robust learning under noisy real- world conditions, and the functional role of the cerebellum. In addition, the gained insights from this research may be exploitable in terms of control of novel actuators in the emerging field of soft robotics. Finally, the presented architectures, including hardware and software infrastructures, provide a platform with which to explore other advanced models of brain mediated sensory-motor control interfaces.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available