Submission Type
Event
Expected Graduation Date
2010
Location
Room E102, Center for Natural Science, Illinois Wesleyan University
Start Date
4-10-2010 10:00 AM
End Date
4-10-2010 11:00 AM
Disciplines
Artificial Intelligence and Robotics | Computer Sciences
Abstract
The problem of robot navigation involves planning a path to move a robot from a start point to a known target point within an obstacle course. The efficiency of such an algorithm can be measured in several ways. For instance, Lumelsky and Stepanov measure the length of the path taken in terms of obstacle perimeters. Gabriely and Rimon compare their two-dimensional algorithm's efficiency to that of the optimal algorithm. Brown Kramer and Sabalka expand upon the work of Gabriely and Rimon to produce an algorithm for dimensions greater than two. The primary objective of this research was to implement improvements in Brown Kramer and Sabalka's algorithm called Boxes, which performs a depth-first search of a discretized obstacle space. Enhancements such as subdivision of the obstacle space and the maximal coloring improvement improve the efficiency of the algorithm significantly.
Included in
Optimization and Analysis of a Robotic Navigational Algorithm
Room E102, Center for Natural Science, Illinois Wesleyan University
The problem of robot navigation involves planning a path to move a robot from a start point to a known target point within an obstacle course. The efficiency of such an algorithm can be measured in several ways. For instance, Lumelsky and Stepanov measure the length of the path taken in terms of obstacle perimeters. Gabriely and Rimon compare their two-dimensional algorithm's efficiency to that of the optimal algorithm. Brown Kramer and Sabalka expand upon the work of Gabriely and Rimon to produce an algorithm for dimensions greater than two. The primary objective of this research was to implement improvements in Brown Kramer and Sabalka's algorithm called Boxes, which performs a depth-first search of a discretized obstacle space. Enhancements such as subdivision of the obstacle space and the maximal coloring improvement improve the efficiency of the algorithm significantly.