원문정보
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
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