Earticle

초록

영어

Finding optimized path in the workspace is one of the fundamental problems to solve for an autonomous mobile robot. Avoiding obstacles and building an efficient trajectory is the key goal. For this reason, a mobile robot has to manage the free configuration space very efficiently for the purpose of path planning and navigation. Partitioning the configuration space will make the path planning task easy, faster and efficient. Also, data read by the sensor has some inherent noise. So we implement an algorithm to make an efficient estimation of the future states to build map that helps manage the environment efficiently to find the optimized paths to destination. We apply Extended Kalman Filter (EKF) to find the accurate estimation on sensor data and then K-means clustering algorithm to find the next landmarks avoiding the obstacles.

목차

Abstract
 1: Introduction
 2: Workspace and Conguration Space
 3: Extended Kalman Filter (EKF)
 4: K-means Clustering Algorithm
 5: Proposed Method
 6: Experimental Results
  6.1: Environment with non-linear Boundary
  6.2: Environment with Large Set of Obstacles
 7: Conclusions
 Acknowledgements
 References

키워드

저자

• Md Nasir Uddin Laskar [ Artificial Intelligence Lab, Dept. of Computer Engineering Kyung Hee University ]
• Hoang Huu Viet [ Artificial Intelligence Lab, Dept. of Computer Engineering Kyung Hee University ]
• TaeChoong Chung [ Artificial Intelligence Lab, Dept. of Computer Engineering Kyung Hee University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605