Object tracking algorithm of Swarm Robot System for using Polygon basedQ-learning and parallel SVM
- Authors
- 서상욱; 심귀보; 양현창
- Issue Date
- 2008
- Publisher
- 한국지능시스템학회
- Keywords
- DBAM; ABAM; Parallel SVM; Polygon; Q-learning
- Citation
- International Journal of Fuzzy Logic and Intelligent systems, v.8, no.3, pp 220 - 224
- Pages
- 5
- Journal Title
- International Journal of Fuzzy Logic and Intelligent systems
- Volume
- 8
- Number
- 3
- Start Page
- 220
- End Page
- 224
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/31182
- ISSN
- 1598-2645
- Abstract
- This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. 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 parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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