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Design of protocol for collaborative...

Zhang, Lu.

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Design of protocol for collaborative task assignment in heterogeneous robotic material handling systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Design of protocol for collaborative task assignment in heterogeneous robotic material handling systems./
Author:	Zhang, Lu.
Description:	92 p.
Notes:	Source: Masters Abstracts International, Volume: 55-02.
Contained By:	Masters Abstracts International55-02(E).
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1603165
ISBN:	9781339206820

Design of protocol for collaborative task assignment in heterogeneous robotic material handling systems.
Zhang, Lu.

Design of protocol for collaborative task assignment in heterogeneous robotic material handling systems. - 92 p.

Source: Masters Abstracts International, Volume: 55-02.

Thesis (M.S.I.E.)--Purdue University, 2015.

Heterogeneity in robot model mix is advantageous in emerging robotic material handling systems. For instance, in a fully automated industrial package loading and unloading scenario, a variety uncertain types of packages need to be continuously sorted and loaded onto designated trucks. To handle the variation of tasks, multiple types of robots, or heterogeneous robots, are designed in the system. However, in such a collaborative system consisting of heterogeneous robots, ineffective task assignments often lead to bad collaboration and thus poor efficiency. In this thesis, to improve the robot collaboration, the collaborative task assignment problem is defined; and a protocol with fuzzy collaborative intelligence to optimize the assignment plans is developed. Specifically, the collaboration type, the collaboration matrix and the assignment matrix are specified; and the model for adaptive fuzzy collaborative task assignment relies on intuitionistic fuzzy set theory. An unsorted package loading and unloading tasks by heterogeneous robots are used as a case example to validate the new method. Experiments indicate with statistical significance that the new approach shortens total completion time by 21%, reduces total energy consumption by 23%, and increases loading accuracy by 31%, compared with the traditional static task assignment method commonly practiced. The developed approach can be applied to different emerging collaborative systems to improve systems' collaborative intelligence.

ISBN: 9781339206820Subjects--Topical Terms:

586835
Engineering.

Design of protocol for collaborative task assignment in heterogeneous robotic material handling systems.
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020 $a 9781339206820
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100 1 $a Zhang, Lu. $3 896980
245 1 0 $a Design of protocol for collaborative task assignment in heterogeneous robotic material handling systems.
300 $a 92 p.
500 $a Source: Masters Abstracts International, Volume: 55-02.
500 $a Adviser: Shimon Y. Nof.
502 $a Thesis (M.S.I.E.)--Purdue University, 2015.
520 $a Heterogeneity in robot model mix is advantageous in emerging robotic material handling systems. For instance, in a fully automated industrial package loading and unloading scenario, a variety uncertain types of packages need to be continuously sorted and loaded onto designated trucks. To handle the variation of tasks, multiple types of robots, or heterogeneous robots, are designed in the system. However, in such a collaborative system consisting of heterogeneous robots, ineffective task assignments often lead to bad collaboration and thus poor efficiency. In this thesis, to improve the robot collaboration, the collaborative task assignment problem is defined; and a protocol with fuzzy collaborative intelligence to optimize the assignment plans is developed. Specifically, the collaboration type, the collaboration matrix and the assignment matrix are specified; and the model for adaptive fuzzy collaborative task assignment relies on intuitionistic fuzzy set theory. An unsorted package loading and unloading tasks by heterogeneous robots are used as a case example to validate the new method. Experiments indicate with statistical significance that the new approach shortens total completion time by 21%, reduces total energy consumption by 23%, and increases loading accuracy by 31%, compared with the traditional static task assignment method commonly practiced. The developed approach can be applied to different emerging collaborative systems to improve systems' collaborative intelligence.
590 $a School code: 0183.
650 4 $a Engineering. $3 586835
690 $a 0537
710 2 $a Purdue University. $b Industrial Engineering. $3 1035836
773 0 $t Masters Abstracts International $g 55-02(E).
790 $a 0183
791 $a M.S.I.E.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1603165