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.

Date Published: 4 February 2013
PDF: 9 pages
Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 86620O (4 February 2013); doi: 10.1117/12.2010720

Published in SPIE Proceedings Vol. 8662:
Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)