Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530602
Title: Continuous local motion planning & control for unmanned vehicle operation within complex obstacle-rich environments
Author: Berry, Andrew James
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2011
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Abstract:
This thesis considers the guidance and control of unmanned vehicles within complex environments. A systems engineering approach was adopted where significant effort was directed towards defining a high level capability requirement and subsequent problem exploration, decomposition and definition, prior to addressing the technical focus. The goal of this approach was to ensure that technical work was directed towards realistic end user requirements and operational scenarios. As the complexity of an operational environment increases, so does the requirement to consider the local obstacle space continually, and this is aided by splitting the motion planning functionality into distinct global and local layers. The technical focus of this thesis is on the development and simulation-based testing of a new local motion planning and control framework, where knowledge of i) feasible vehicle manoeuvre constraints ii) local obstacle map iii) current environment conditions are all combined into a continuous receding horizon approach. This framework separates the output and control space elements of the problem, reducing the complexity of the local motion trajectory optimisation and therefore enabling faster design and increased horizon length. Bezier polynomial functions are used to describe local motion trajectories which are constrained to vehicle performance limits and optimised to achieve a specified goal. The primary problem addressed is ‘situation-aware’ trajectory tracking, but other local motion planning modes are also considered. Development and testing of the new framework is undertaken within simulation (Matlab), based on a nonlinear 6 degree of freedom model of a quadrotor unmanned air vehicle. Situation-aware trajectory tracking is demonstrated in the presence of static and dynamic obstacles, as well as the presence of realistic turbulence and gusts. The immediate-term deconfliction of multiple unmanned vehicles, and multiple formations of unmanned vehicles, is also demonstrated, including the provision of rules-of-the-air type behaviour
Supervisor: Postlethwaite, Ian ; Gu, Da-Wei ; Howitt, Jeremy Sponsor: QinetiQ
Qualification Name: EngD Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.530602  DOI: Not available
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