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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