R-Mastif: Robotic Mobile Autonomous System For Threat Interrogation And Object Fetch

Autonomous robotic "fetch" operation, where a robot is shown a novel object and then asked to locate it in the field, retrieve 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 previously unseen objects out to 15 meters on moderately flat terrain. We integrated SRI's state of the art Visual Odometry for UPS-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.