R-MASTIF: Robotic mobile autonomous system for threat interrogation and object fetch

Aveek Das, Dinesh Thakur, James Keller, Sujit Kuthirummal, Zsolt Kira, and Mihail Pivtoraiko. R-MASTIF: Robotic mobile autonomous system for threat interrogation and object fetch. In Proceedings of the SPIE Conference, 2013.

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Abstract

Autonomous robotic “fetch” operation, where a robot is shown a novel object and then asked to locate it in the field, re- trieve it and bring it back to the human operator, is a challenging problem that is of interest to the military. The CANINE competition presented a forum for several research teams to tackle this challenge using state of the art in robotics technol- ogy. The SRI-UPenn team fielded a modified Segway RMP 200 robot with multiple cameras and lidars. We implemented a unique computer vision based approach for textureless colored object training and detection to robustly locate previ- ously unseen objects out to 15 meters on moderately flat terrain. We integrated SRI’s state of the art Visual Odometry for GPS-denied localization on our robot platform. We also designed a unique scooping mechanism which allowed retrieval of up to basketball sized objects with a reciprocating four-bar linkage mechanism. Further, all software, including a novel target localization and exploration algorithm was developed using ROS (Robot Operating System) which is open source and well adopted by the robotics community. We present a description of the system, our key technical contributions and experimental results.

BibTeX

@INPROCEEDINGS{das_etal_spie13,
  author = {Aveek Das and Dinesh Thakur and James Keller and Sujit Kuthirummal
	and Zsolt Kira and Mihail Pivtoraiko},
  title = {{R-MASTIF}: Robotic mobile autonomous system for threat interrogation
	and object fetch},
  abstract = {Autonomous robotic “fetch” operation, where a robot is
                  shown a novel object and then asked to locate it in
                  the field, re- trieve it and bring it back to the
                  human operator, is a challenging problem that is of
                  interest to the military. The CANINE competition
                  presented a forum for several research teams to
                  tackle this challenge using state of the art in
                  robotics technol- ogy. The SRI-UPenn team fielded a
                  modified Segway RMP 200 robot with multiple cameras
                  and lidars. We implemented a unique computer vision
                  based approach for textureless colored object
                  training and detection to robustly locate previ-
                  ously unseen objects out to 15 meters on moderately
                  flat terrain. We integrated SRI’s state of the art
                  Visual Odometry for GPS-denied localization on our
                  robot platform. We also designed a unique scooping
                  mechanism which allowed retrieval of up to
                  basketball sized objects with a reciprocating
                  four-bar linkage mechanism. Further, all software,
                  including a novel target localization and
                  exploration algorithm was developed using ROS (Robot
                  Operating System) which is open source and well
                  adopted by the robotics community. We present a
                  description of the system, our key technical
                  contributions and experimental results.},
  booktitle = {Proceedings of the SPIE Conference},
  year = {2013},
  bib2html_pubtype = {Workshop Papers}, 
  bib2html_rescat = {Coverage Planning}
}

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