Dissertation/Thesis Abstract

Path planning using ant colony optimization algorithm
by Shao, Jingqing, M.S., California State University, Long Beach, 2011, 90; 1493185
Abstract (Summary)

In this thesis we study the problem of path finding in an environment with obstacles. The ACO algorithm is used to identify an optimal path for a given pair of end points. Comer detection is used to detect the obstacles and provide the source points to the ACO algorithm.

Both qualitative and quantitative research methodology was utilized in this paper. The qualitative research data consisted of six images. The quantitative research data was conducted with the aid of statistic chart and tables. Executions on all six data sample images were monitored and the results were represented using the charts and tables.

The results showed that ACO algorithm is adequate in finding the shortest path through various obstacles without violating any boundaries. The boundary infonnation of obstacles was well-preserved and violation of boundary was monitored.

The author recommends that in the future work the corner detector would be perfected to achieve higher performance, such as adding clustering methods.

Indexing (document details)
Advisor: Nguyen, Thinh
School: California State University, Long Beach
School Location: United States -- California
Source: MAI 49/05M, Masters Abstracts International
Subjects: Artificial intelligence, Computer science
Publication Number: 1493185
ISBN: 978-1-124-62222-4
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