R-MASTIF: Robotic Mobile Autonomous System for Threat Interrogation and Object Fetch


Aveek Das ; Dinesh Thakur ; James Keller ; Sujit Kuthirummal ; Zsolt Kira, et al.” R-MASTIF: robotic mobile autonomous system for threat interrogation and object fetch “, Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 86620O (February 4, 2013); doi:10.1117/12.2010720; http://dx.doi.org/10.1117/12.2010720


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 technology. 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.

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