다각형 기반의 Q-learning과 Cascade SVM을 이용한 군집로봇의 목표물 추적 알고리즘Object tracking algorithm of Swarm Robot System for using Polygon based Q-learning and Cascade SVM
- Authors
- 서상욱; 양현창; 심귀보
- Issue Date
- 2008
- Publisher
- 대한임베디드공학회
- Keywords
- Cascade SVM; Polygon; Q-learning; DBAM; ABAM
- Citation
- 대한임베디드공학회논문지, v.3, no.2, pp 119 - 125
- Pages
- 7
- Journal Title
- 대한임베디드공학회논문지
- Volume
- 3
- Number
- 2
- Start Page
- 119
- End Page
- 125
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/31381
- ISSN
- 1975-5066
- Abstract
- This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and Cascade SVM to enhance the fusion model with DBAM and ABAM process.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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