Abstract
Path planning has been studied focusing on finding the shortest paths or smallest movements. The previous methods, however, are not suitable for stable movements on real environments in which various dynamic obstacles exist. In this paper, we suggest a path planning algorithm that makes the movement of an autonomous robot easier in a dynamic environment. Our focus is based on finding optimal movements for mobile robot to keep going on a stable situation but not on finding shortest paths or smallest movements. The proposed algorithm is based on GA and uses kmeans cluster analysis algorithm to recognize the much more information of obstacles distribution in real-life space. Simulation results confirmed to have better performance and stability of the proposed algorithm. In order to validate our results, we compared with a previous algorithm based on grid maps-based algorithm for static obstacles and dynamic obstacles environment.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10 |
| Pages | 959-962 |
| Number of pages | 4 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan Duration: Feb 4 2010 → Feb 6 2010 |
Other
| Other | 15th International Symposium on Artificial Life and Robotics, AROB '10 |
|---|---|
| Country | Japan |
| City | Beppu, Oita |
| Period | 2/4/10 → 2/6/10 |
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Keywords
- Clustering and static/dynamic obstacles
- GA
- Path Planning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
Cite this
Path planning algorithm using the values clustered by k-means. / Kang, Won Seok; Lee, Seung Hyun; Abibullaev, Berdakh; Kim, Jin Wook; An, Jinung.
Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10. 2010. p. 959-962.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Path planning algorithm using the values clustered by k-means
AU - Kang, Won Seok
AU - Lee, Seung Hyun
AU - Abibullaev, Berdakh
AU - Kim, Jin Wook
AU - An, Jinung
PY - 2010
Y1 - 2010
N2 - Path planning has been studied focusing on finding the shortest paths or smallest movements. The previous methods, however, are not suitable for stable movements on real environments in which various dynamic obstacles exist. In this paper, we suggest a path planning algorithm that makes the movement of an autonomous robot easier in a dynamic environment. Our focus is based on finding optimal movements for mobile robot to keep going on a stable situation but not on finding shortest paths or smallest movements. The proposed algorithm is based on GA and uses kmeans cluster analysis algorithm to recognize the much more information of obstacles distribution in real-life space. Simulation results confirmed to have better performance and stability of the proposed algorithm. In order to validate our results, we compared with a previous algorithm based on grid maps-based algorithm for static obstacles and dynamic obstacles environment.
AB - Path planning has been studied focusing on finding the shortest paths or smallest movements. The previous methods, however, are not suitable for stable movements on real environments in which various dynamic obstacles exist. In this paper, we suggest a path planning algorithm that makes the movement of an autonomous robot easier in a dynamic environment. Our focus is based on finding optimal movements for mobile robot to keep going on a stable situation but not on finding shortest paths or smallest movements. The proposed algorithm is based on GA and uses kmeans cluster analysis algorithm to recognize the much more information of obstacles distribution in real-life space. Simulation results confirmed to have better performance and stability of the proposed algorithm. In order to validate our results, we compared with a previous algorithm based on grid maps-based algorithm for static obstacles and dynamic obstacles environment.
KW - Clustering and static/dynamic obstacles
KW - GA
KW - Path Planning
UR - http://www.scopus.com/inward/record.url?scp=84866717334&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866717334&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9784990288044
SP - 959
EP - 962
BT - Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
ER -