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Visual Sensing for Developing Autonomous Behavior in Snake Robots

Hugo Ponte, Max Queenan, and Christoph Mertz, Matthew J. Travers, Florian Enner, Martial Hebert and Howie Choset
Conference Paper, Carnegie Mellon University, 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), June, 2014

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Abstract

Snake robots are uniquely qualified to investigate a large variety of settings including archaeological sites, natural disaster zones, and nuclear power plants. For these applications, modular snake robots have been tele-operated to perform specific tasks using images returned to it from an onboard camera in the robots head. In order to give the operator an even richer view of the environment and to enable the robot to perform autonomous tasks we developed a structured light sensor that can make three-dimensional maps of the environment. This paper presents a sensor that is uniquely qualified to meet the severe constraints in size, power and computational footprint of snake robots. Using range data, in the form of 3D pointclouds, we show that it is possible to pair high-level planning with mid-level control to accomplish complex tasks without operator intervention.

BibTeX Reference
@conference{Ponte-2014-7876,
title = {Visual Sensing for Developing Autonomous Behavior in Snake Robots},
author = {Hugo Ponte and Max Queenan and and Christoph Mertz and Matthew J. Travers and Florian Enner and Martial Hebert and Howie Choset},
booktitle = {2014 IEEE International Conference on Robotics and Automation (ICRA 2014)},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2014},
address = {Pittsburgh, PA},
}
2017-09-13T10:38:59+00:00