Replanning of multiple autonomous vehicles in material handling

Paul, G; Liu, DK

Replanning of multiple autonomous vehicles in material handling

Paul, G

Liu, DK

Permalink

Publication Type:: Conference Proceeding
Citation:: 2006 IEEE Conference on Robotics, Automation and Mechatronics, 2006
Issue Date:: 2006-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (299.64 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Paul, G https://orcid.org/0000-0002-3478-0020	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	2006 IEEE Conference on Robotics, Automation and Mechatronics, 2006	en_US
dc.identifier.isbn	1424400244	en_US
dc.identifier.isbn	9781424400249	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2896
dc.description.abstract	The fully automated docks in Australia present opportunities for applications of autonomous vehicles and engineering innovation. When planning tasks to be done by multi-autonomous vehicles in an enclosed area with a known dynamic map (i.e. bi-directional path network), there are many issues that have not yet been comprehensively solved. The real world presents more complexity than the initial algorithms addressed. There are problems that occur due to interaction with the real-world. This means autonomous vehicles can stop, are affected, or face problems, and hence tasks and vehicles' paths and motion need to be replanned. In order to replan, a greater understanding of the state of vehicles, the state of the map, and importantly the importance of tasks and vehicles is definitely needed. This paper explores the improvements made to replanning by gaining a thorough understanding of the map and then utilising map information to make the best, most efficient replanning decision. Five replanning Methods are investigated and four Options which combine the Methods in different ways, are tested in this research. A map analysis Method is also presented. Simulation studies show that map information based replanning is the most efficient Method out of those tested. © 2006 IEEE.	en_US
dc.relation.ispartof	2006 IEEE Conference on Robotics, Automation and Mechatronics	en_US
dc.relation.isbasedon	10.1109/RAMECH.2006.252729	en_US
dc.title	Replanning of multiple autonomous vehicles in material handling	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
dc.location.activity	Bangkok, Thailand	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

The fully automated docks in Australia present opportunities for applications of autonomous vehicles and engineering innovation. When planning tasks to be done by multi-autonomous vehicles in an enclosed area with a known dynamic map (i.e. bi-directional path network), there are many issues that have not yet been comprehensively solved. The real world presents more complexity than the initial algorithms addressed. There are problems that occur due to interaction with the real-world. This means autonomous vehicles can stop, are affected, or face problems, and hence tasks and vehicles' paths and motion need to be replanned. In order to replan, a greater understanding of the state of vehicles, the state of the map, and importantly the importance of tasks and vehicles is definitely needed. This paper explores the improvements made to replanning by gaining a thorough understanding of the map and then utilising map information to make the best, most efficient replanning decision. Five replanning Methods are investigated and four Options which combine the Methods in different ways, are tested in this research. A map analysis Method is also presented. Simulation studies show that map information based replanning is the most efficient Method out of those tested. © 2006 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/2896