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High level classification recommended decision making for Autonomous Ground Vehicle (AGV)
Rehman, Naeem Ur1; Asghar, Sohail2; Usman, Muhammad3; Fong, Simon4; Cho, Kyungeun5; Park, Yong Woon6
2016-07-01
Source PublicationJournal of Computational and Theoretical Nanoscience
ISSN1546-1955
Volume13Issue:7Pages:4284-4292
Abstract

An Autonomous Ground Vehicle (AGV) should be capable of self-navigating through various terrains based on priori data as well as self-configuring and optimizing its motion on the basis of sensed data. Research is in progress to improve terrain perception for planning, execution, and control of desired motion of an AGV. During the perception phase multiple classification techniques are used depending on underlying sensing technology. Obstacle detection in case of a compositetyped terrain is a challenging task because in order to apply classification the image has to be known as a single type. Image segmentation and then classifying each image-segment separately can help AGV proceed in the same direction (by selecting another path) even if it detects an obstacle in the image. This paper proposes a fuzzy classification scheme for terrain identification and obstacle detection to improve self-organization according to terrain type. In order to take an accurate decision, classified objects coming from the perception phase of different sensors need to be fused into a single accurate representation for both the environment and the obstacle. Moreover, we provide means for intelligent decision making in the selection of sensors, fusion of sensor data, assessment of obstacle state and direction. Finally, the evaluation of a recommended decision has been performed for the vehicle speed and direction.

KeywordAutonomous Ground Vehicle Classification Fusion Fuzzy Rules Kalman Filter
DOI10.1166/jctn.2016.5282
URLView the original
Language英語English
PublisherAmerican Scientific Publishers
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Department of Computer Science, Muhammad Ali Jinnah University Islamabad, Paksitan
2.Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan
3.Department of Computing, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
4.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
5.Department of Multimedia Engineering, Dongguk University, Seoul, Republic of Korea
6.Agency for Defense Development, Daejeon, Republic of Korea
Recommended Citation
GB/T 7714
Rehman, Naeem Ur,Asghar, Sohail,Usman, Muhammad,et al. High level classification recommended decision making for Autonomous Ground Vehicle (AGV)[J]. Journal of Computational and Theoretical Nanoscience,2016,13(7):4284-4292.
APA Rehman, Naeem Ur,Asghar, Sohail,Usman, Muhammad,Fong, Simon,Cho, Kyungeun,&Park, Yong Woon.(2016).High level classification recommended decision making for Autonomous Ground Vehicle (AGV).Journal of Computational and Theoretical Nanoscience,13(7),4284-4292.
MLA Rehman, Naeem Ur,et al."High level classification recommended decision making for Autonomous Ground Vehicle (AGV)".Journal of Computational and Theoretical Nanoscience 13.7(2016):4284-4292.
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