Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492629
Title: Linguistic decision tree cloning of optimised trajectories for real-time obstacle avoidance
Author: Turnbull, Oliver
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2008
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Abstract:
Linguistic Decision Trees (LDTs) are used to clone the behaviour of a Model Predictive Control (MPC) algorithm for obstacle avoidance. The resulting controller benefits from the optimised trajectories of the MPC and the rapid computation of the decision tree to provide decisions that are suitable for use in a real-time dynamic environment. The LDT represents a set of linguistic decision rules that ensure a high degree of controller transparency. A method to predict discontinuous functions, such as UAV heading deviation required to avoid an obstacle, is proposed and shown to significantly improve the controller's performance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.492629  DOI: Not available
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