Visual Sensing for Developing Autonomous Behavior in Snake Robots

Hugo Ponte, Max Queenan, Chaohui Gong, Christoph Mertz, Matthew J. Travers, Florian Enner, Martial Hebert, and Howie Choset
2014 IEEE International Conference on Robotics and Automation (ICRA 2014), July, 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.

Notes

Text Reference
Hugo Ponte, Max Queenan, Chaohui Gong, Christoph Mertz, Matthew J. Travers, Florian Enner, Martial Hebert, and Howie Choset, "Visual Sensing for Developing Autonomous Behavior in Snake Robots," 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), July, 2014.

BibTeX Reference
@inproceedings{Ponte_2014_7578,
   author = "Hugo Ponte and Max Queenan and Chaohui Gong and Christoph Mertz and Matthew J Travers and Florian Enner and Martial Hebert and Howie Choset",
   title = "Visual Sensing for Developing Autonomous Behavior in Snake Robots",
   booktitle = "2014 IEEE International Conference on Robotics and Automation (ICRA 2014)",
   month = "July",
   year = "2014",
}