Development of the "Discretized Dynamic Expanding Zones with Memory" Autonomous Mobility Algorithm for the Nemesis Tracked Vehicle Platform

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dc.contributor.authorGothing, Grant Edwarden
dc.contributor.committeechairReinholtz, Charles F.en
dc.contributor.committeecochairWicks, Alfred L.en
dc.contributor.committeememberHong, Dennis W.en
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2014-03-14T20:42:58Zen
dc.date.adate2007-10-10en
dc.date.available2014-03-14T20:42:58Zen
dc.date.issued2007-07-30en
dc.date.rdate2007-10-10en
dc.date.sdate2007-08-07en
dc.description.abstractThe 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
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-08072007-081735en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08072007-081735/en
dc.identifier.urihttp://hdl.handle.net/10919/34404en
dc.publisherVirginia Techen
dc.relation.haspartGothing_Thesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectObstacle Memoryen
dc.subjectJAUSen
dc.subjectUnmanneden
dc.subjectDynamic Expanding Zonesen
dc.subjectCollaborationen
dc.subjectAutonomous Vehiclesen
dc.titleDevelopment of the "Discretized Dynamic Expanding Zones with Memory" Autonomous Mobility Algorithm for the Nemesis Tracked Vehicle Platformen
dc.typeThesisen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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