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dc.contributor.authorGothing, Grant Edwarden_US
dc.date.accessioned2014-03-14T20:42:58Z
dc.date.available2014-03-14T20:42:58Z
dc.date.issued2007-07-30en_US
dc.identifier.otheretd-08072007-081735en_US
dc.identifier.urihttp://hdl.handle.net/10919/34404
dc.description.abstract

The Nemesis tracked vehicle platform is a differentially driven Humanitarian Demining tractor developed by Applied Research Associates, Inc. The vehicle is capable of teleoperational control and is outfitted with a sensor suite used for detecting and neutralizing landmines. Because the detection process requires the vehicle to travel at speeds less than 0.5 km/h, teleoperation is a tedious process. The added autonomous capabilities of waypoint navigation and obstacle avoidance could greatly reduce operator fatigue.

ARA chose to leverage Virginia Tech's experience in developing an autonomous mobility capability for the Nemesis platform. The resulting algorithms utilize the waypoint navigation techniques of Virginia Tech's JAUS (Joint Architecture for Unmanned Systems) toolkit, and a modified version of the Dynamic Expanding Zones (DEZ) algorithm developed for the 2005 DARPA Grand Challenge. The modified approach discretizes the perception zones of the DEZ algorithm and provides the added capability of obstacle memory, resulting in the Discretized Dynamic Expanding Zones with Memory (DDEZm) algorithm. These additions are necessary for efficient autonomous control of the differentially driven Nemesis vehicle.

The DDEZm algorithm was coded in LabVIEW and used to autonomously navigate the Nemesis vehicle through a waypoint course while avoiding obstacles. The Joint Architecture for Unmanned Systems (JAUS) was used as the communication standard to facilitate the interoperability between the software developed at Virginia Tech and the existing Nemesis software developed by ARA. In addition to development and deployment, the algorithm has been fully documented for embedded coding by a software engineer. With embedded implementation on the vehicle, this algorithm will help to increase the efficiency of the landmine detection process, ultimately saving lives.

en_US
dc.publisherVirginia Techen_US
dc.relation.haspartGothing_Thesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectObstacle Memoryen_US
dc.subjectJAUSen_US
dc.subjectUnmanneden_US
dc.subjectDynamic Expanding Zonesen_US
dc.subjectCollaborationen_US
dc.subjectAutonomous Vehiclesen_US
dc.titleDevelopment of the "Discretized Dynamic Expanding Zones with Memory" Autonomous Mobility Algorithm for the Nemesis Tracked Vehicle Platformen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineMechanical Engineeringen_US
dc.contributor.committeechairReinholtz, Charles F.en_US
dc.contributor.committeememberHong, Dennis W.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08072007-081735/en_US
dc.contributor.committeecochairWicks, Alfred L.en_US
dc.date.sdate2007-08-07en_US
dc.date.rdate2007-10-10
dc.date.adate2007-10-10en_US


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