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Bio-Inspired Cooperative Optimal Trajectory Planning for Autonomous Vehicles

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Date Issued:
2013
Abstract/Description:
With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles.
Title: Bio-Inspired Cooperative Optimal Trajectory Planning for Autonomous Vehicles.
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Name(s): Remeikas, Charles, Author
Xu, Yunjun, Committee Chair
Kassab, Alain, Committee Member
Lin, Kuo-Chi, Committee Member
, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2013
Publisher: University of Central Florida
Language(s): English
Abstract/Description: With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles.
Identifier: CFE0005053 (IID), ucf:49978 (fedora)
Note(s): 2013-12-01
M.S.A.E.
Engineering and Computer Science, Mechanical and Aerospace Engineering
Masters
This record was generated from author submitted information.
Subject(s): automation -- cooperative control -- optimal control -- formation control -- trajectory planning -- bio-inspired
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005053
Restrictions on Access: campus 2016-12-15
Host Institution: UCF

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