NDLI: Vision-based leader-follower formations with limited information

Content Provider	IEEE Xplore Digital Library
Author	Min, Hyeun Jeong Drenner, Andrew Papanikolopoulos, Nikolaos
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, 55455, USA (Min, Hyeun Jeong; Drenner, Andrew; Papanikolopoulos, Nikolaos)
Abstract	This paper presents a new vision-based leader-follower formation algorithm where the leader's trajectory is unknown to the robots which are following. Formation schemes in straight lines and diagonal formations are introduced which are both stable and observable in the presence of limited views. The algorithms are novel since they only use local image measurements through a pinhole camera to estimate the leader's position. This approach does not require specialized markings nor extensive robot communications. The algorithms are also decentralized. We apply an input-output feedback linearization for system stability and utilize an Extended Kalman Filter (EKF) for estimation. Simulations illustrate how the proposed formation controls work. Real experiments utilizing multiple miniature robots are also presented and illustrate the challenges associated with noisy images in real-world applications.
Starting Page	351
Ending Page	356
File Size	476086
Page Count	6
File Format	PDF
ISBN	9781424427888
ISSN	10504729
DOI	10.1109/ROBOT.2009.5152261
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Robot vision systems Communication system control Robot sensing systems Kinematics Centralized control Linear feedback control systems Stability Angular velocity Velocity control
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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