Scanning the Environment With Two Independent Cameras - Biologically Motivated Approach
.
Scanning the Environment with two Independent Cameras - Biologically Motivated Approach.
In Proc. Of the IEEE/RSJ International Conf. on Intelligent Robots and Systems, 2006
Online Version
A pdf version is available for download.
Abstract
In this paper we present a novel method for visual scanning and target tracking by means of independent pan-tilt cameras which mimic the chameleon visual system. We present a systematic and optimization-based approach to the problem, from the high-level to low-level control. In particular, in the first part we develop a new algorithm for scanning the sphere using multiple cameras. The algorithm combines information about the environment and amodel of target movement, to perform optimal scanning by means of stochastic dynamic programming. In the second part we develop a model-based control strategy for target tracking. A switching optimal control strategy based on smooth pursuit and saccades is designed by means of explicit Model Predictive Control (MPC) theory. We simulated and experimantally validated our theory on a robotic chameleon head composed of two independent Pan-Tilt cameras. The resulting scanning pattern andtarget tracking has a remarkable resemblance to the one seen in nature by chameleons.
Co-authors
Bibtex Entry
@inproceedings{AvniBKRR06i,
title = {Scanning the Environment with two Independent Cameras - Biologically Motivated Approach .},
author = {Ofir Avni and Francesco Borrelli and Gadi Katzir and Ehud Rivlin and Hector Rotstein},
year = {2006},
month = {October},
booktitle = {Proc. Of the IEEE/RSJ International Conf. on Intelligent Robots and Systems},
abstract = {In this paper we present a novel method for visual scanning and target tracking by means of independent pan-tilt cameras which mimic the chameleon visual system. We present a systematic and optimization-based approach to the problem, from the high-level to low-level control. In particular, in the first part we develop a new algorithm for scanning the sphere using multiple cameras. The algorithm combines information about the environment and amodel of target movement, to perform optimal scanning by means of stochastic dynamic programming. In the second part we develop a model-based control strategy for target tracking. A switching optimal control strategy based on smooth pursuit and saccades is designed by means of explicit Model Predictive Control (MPC) theory. We simulated and experimantally validated our theory on a robotic chameleon head composed of two independent Pan-Tilt cameras. The resulting scanning pattern andtarget tracking has a remarkable resemblance to the one seen in nature by chameleons.}
}