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Formal Methods Based Motion Planning and Control.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Formal Methods Based Motion Planning and Control./
Author:	Cai, Mingyu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	162 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28491613
ISBN:	9798534662108

Formal Methods Based Motion Planning and Control.
Cai, Mingyu.

Formal Methods Based Motion Planning and Control. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 162 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--The University of Iowa, 2021.

This item is not available from ProQuest Dissertations & Theses.

Autonomous systems like household service robots, self-driving cars and drones are emerging as important parts of our daily lives in the near future. It is desirable to specify robotic tasks in a rich and natural high-level language, and have the robot(s) automatically convert the specifications into a set of low-level primitives, such as feedback controllers and communication protocols to accomplish the task. In this dissertation, we employ formal methods to describe complex motion planning tasks, rather than the well-studied point-to-point navigation in traditional control problems. We bring ideas from formal verification and hybrid control to build a framework in which probably correct motion control laws can be generated to fulfill complex missions in uncertain and dynamic environments.

ISBN: 9798534662108Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Control theory

Formal Methods Based Motion Planning and Control.
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100 1 $a Cai, Mingyu. $3 3681665
245 1 0 $a Formal Methods Based Motion Planning and Control.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 162 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Xiao, Shaoping;Kan, Zhen.
502 $a Thesis (Ph.D.)--The University of Iowa, 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Autonomous systems like household service robots, self-driving cars and drones are emerging as important parts of our daily lives in the near future. It is desirable to specify robotic tasks in a rich and natural high-level language, and have the robot(s) automatically convert the specifications into a set of low-level primitives, such as feedback controllers and communication protocols to accomplish the task. In this dissertation, we employ formal methods to describe complex motion planning tasks, rather than the well-studied point-to-point navigation in traditional control problems. We bring ideas from formal verification and hybrid control to build a framework in which probably correct motion control laws can be generated to fulfill complex missions in uncertain and dynamic environments.
590 $a School code: 0096.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Robotics. $3 519753
650 4 $a Electrical engineering. $3 649834
650 4 $a Artificial intelligence. $3 516317
650 4 $a Robots. $3 529507
650 4 $a Simulation. $3 644748
650 4 $a Temporal logic. $3 3681666
650 4 $a Planning. $3 552734
650 4 $a Case studies. $2 itrt $3 996239
653 $a Control theory
653 $a Machine learning
653 $a Neural network
653 $a Optimization
690 $a 0548
690 $a 0771
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690 $a 0800
710 2 $a The University of Iowa. $b Mechanical Engineering. $3 1683754
773 0 $t Dissertations Abstracts International $g 83-02B.
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791 $a Ph.D.
792 $a 2021
793 $a English
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