Autonomous exploration and mapping of complex 3D environments by means of a 6DOF manipulator

Paul, GD

Autonomous exploration and mapping of complex 3D environments by means of a 6DOF manipulator

Paul, GD

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Publication Type:: Thesis
Issue Date:: 2010

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Field	Value	Language
dc.contributor.author	Paul, GD
dc.date.accessioned	2010-10-26T04:10:24Z
dc.date.accessioned	2012-12-15T03:53:13Z
dc.date.available	2010-10-26T04:10:24Z
dc.date.available	2012-12-15T03:53:13Z
dc.date.issued	2010
dc.identifier.uri	http://hdl.handle.net/10453/20287
dc.description	University of Technology, Sydney. Faculty of Engineering and Information Technology.
dc.description.abstract	The futuristic vision of industrial robotic systems that operate in complex, unstructured and diverse environments is beginning to become a reality due to the advances in computing, sensing and control. Automatically acquiring the structure and the properties of an environment in a timely manner is one of the key tasks that need to be accomplished in many field robotics applications. This thesis presents a novel and efficient approach to the exploration of three-dimensional (3D) environments using an industrial robot manipulator. The approach presented combines the objectives of 3D map building and surface material- type identifcation. The manipulator is manoeuvred through a sequence of viewpoints that are selected to maximise the quality of the map generated, minimise the time taken for the exploration, as well as minimise the uncertainty of the surface material type estimation, all whilst avoiding potential collisions between the manipulator and the environment. The thesis first focuses on acquiring the geometry of surfaces in the environment while exploring the industrial robot manipulator's collision-free configuration space. Ellipsoidal virtual bounding fields are positioned around the manipulator's links so that distance queries can be performed and collisions with obstacles in the environment or unexplored space are avoided. Information theory is used to measure the information remaining on the geometric map and the manipulator's configuration space. A sampling strategy is used to select candidate viewpoints which are predicted to reduce the information remaining to measure. Each viewpoint enables the manipulator to position and orientate a sensor so that environment data can be gathered. The candidate viewpoint solutions can then be ranked based upon the exploration objectives. The collected sensor data is fused into a map. The map is then segmented into groups of Scale-Like Discs (SLDs), which are generated via principal component analysis. Once the surface geometry becomes available, a strategy is required to maximise the accuracy of the surface material-type identification. Surface material-type identification is made possible through intensity measurements, which indicate the refl ectivity of the surface when illuminated by an infra-red laser. Thus, identification is significantly infl uenced by the relative geometry between the sensor and the surface to be identified. Information theory is used again to determine surfaces which have not had their surface material- type identified. Appropriate viewpoints facilitating accurate identification are selected by solving an optimisation problem using the Levenberg-Marquardt algorithm. This two-stage exploration approach is shown to successfully determine viewpoints enabling an accurate environmental map to be generated. The proposed algorithms and approaches are integrated into the system, Autonomous eXploration to Build A Map (AXBAM). Extensive experimental studies have been conducted on a complex steel bridge structure using a Denso industrial robot that has been equipped with a laser range finding sensor. These experimental studies demonstrate the efficacy of the AXBAM system.	en
dc.format	Thesis (PhD)	en
dc.language.iso	en	en
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/20287/2/02Whole.pdf
dc.relation.replaces	http://hdl.handle.net/2100/1163
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	au.edu.uts.lib/ppc
dc.subject	Robots, Industrial.	en
dc.subject	Three-dimensional display systems.	en
dc.title	Autonomous exploration and mapping of complex 3D environments by means of a 6DOF manipulator	en
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

The futuristic vision of industrial robotic systems that operate in complex, unstructured and diverse environments is beginning to become a reality due to the advances in computing, sensing and control. Automatically acquiring the structure and the properties of an environment in a timely manner is one of the key tasks that need to be accomplished in many field robotics applications. This thesis presents a novel and efficient approach to the exploration of three-dimensional (3D) environments using an industrial robot manipulator. The approach presented combines the objectives of 3D map building and surface material- type identifcation. The manipulator is manoeuvred through a sequence of viewpoints that are selected to maximise the quality of the map generated, minimise the time taken for the exploration, as well as minimise the uncertainty of the surface material type estimation, all whilst avoiding potential collisions between the manipulator and the environment. The thesis first focuses on acquiring the geometry of surfaces in the environment while exploring the industrial robot manipulator's collision-free configuration space. Ellipsoidal virtual bounding fields are positioned around the manipulator's links so that distance queries can be performed and collisions with obstacles in the environment or unexplored space are avoided. Information theory is used to measure the information remaining on the geometric map and the manipulator's configuration space. A sampling strategy is used to select candidate viewpoints which are predicted to reduce the information remaining to measure. Each viewpoint enables the manipulator to position and orientate a sensor so that environment data can be gathered. The candidate viewpoint solutions can then be ranked based upon the exploration objectives. The collected sensor data is fused into a map. The map is then segmented into groups of Scale-Like Discs (SLDs), which are generated via principal component analysis. Once the surface geometry becomes available, a strategy is required to maximise the accuracy of the surface material-type identification. Surface material-type identification is made possible through intensity measurements, which indicate the refl ectivity of the surface when illuminated by an infra-red laser. Thus, identification is significantly infl uenced by the relative geometry between the sensor and the surface to be identified. Information theory is used again to determine surfaces which have not had their surface material- type identified. Appropriate viewpoints facilitating accurate identification are selected by solving an optimisation problem using the Levenberg-Marquardt algorithm. This two-stage exploration approach is shown to successfully determine viewpoints enabling an accurate environmental map to be generated. The proposed algorithms and approaches are integrated into the system, Autonomous eXploration to Build A Map (AXBAM). Extensive experimental studies have been conducted on a complex steel bridge structure using a Denso industrial robot that has been equipped with a laser range finding sensor. These experimental studies demonstrate the efficacy of the AXBAM system.

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