Title page for ETD etd-05252006-134900


Type of Document Master's Thesis
Author Putney, Joseph Satoru
Author's Email Address jputney@vt.edu
URN etd-05252006-134900
Title Reactive Navigation of an Autonomous Ground Vehicle Using Dynamic Expanding Zones
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Reinholtz, Charles F. Committee Chair
Hong, Dennis W. Committee Member
Wicks, Alfred L. Committee Member
Keywords
  • Dynamic Expanding Zones
  • Fuzzy Logic
  • Unmanned Ground Vehicle
  • Mobile Robots
  • Autonomous Vehicles
  • Reactive Navigation
Date of Defense 2006-05-11
Availability unrestricted
Abstract
Autonomous navigation of mobile robots through unstructured terrain presents

many challenges. The task becomes even more difficult with increasing obstacle density,

at higher speeds, and when a priori knowledge of the terrain is not available. Reactive

navigation schemas are often dismissed as overly simplistic or considered to be inferior

to deliberative approaches for off-road navigation. The Potential Field algorithm has

been a popular reactive approach for low speed, highly maneuverable mobile robots.

However, as vehicle speeds increase, Potential Fields becomes less effective at avoiding

obstacles.

The traditional shortcomings of the Potential Field approach can be largely

overcome by using dynamically expanding perception zones to help track objects of

immediate interest. This newly developed technique is hereafter referred to as the

Dynamic Expanding Zones (DEZ) algorithm. In this approach, the Potential Field

algorithm is used for waypoint navigation and the DEZ algorithm is used for obstacle

avoidance. This combination of methods facilitates high-speed navigation in obstaclerich

environments at a fraction of the computational cost and complexity of deliberative

methods.

The DEZ reactive navigation algorithm is believed to represent a fundamental

contribution to the body of knowledge in the area of high-speed reactive navigation.

This method was implemented on the Virginia Tech DARPA Grand Challenge vehicles.

The results of this implementation are presented as a case study to demonstrate the

efficacy of the newly developed DEZ approach.

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