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首页> 外文会议>Intelligent robots and computer vision XXX: algorithms and techniques >R-MASTIF: Robotic mobile autonomous system for threat interrogation and object fetch
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R-MASTIF: Robotic mobile autonomous system for threat interrogation and object fetch

机译：R-MASTIF：机器人移动自主系统，用于威胁询问和对象获取

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摘要

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 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.
机译：自主的机器人“获取”操作是向机器人展示一个新颖的物体，然后要求其在野外定位，检索并将其带回给操作人员的自动化操作，这是军方感兴趣的具有挑战性的问题。 CANINE竞赛为多个研究团队提供了一个论坛，以利用机器人技术的最新水平来应对这一挑战。 SRI-UPenn团队部署了带有多个摄像头和激光雷达的改良Segway RMP 200机器人。我们实施了一种基于计算机视觉的独特方法来进行无纹理的有色物体训练和检测，以将以前看不见的物体牢固地定位在中等平坦的地形上15米之外。我们在机器人平台上集成了SRI先进的视觉里程表技术，以实现GPS定位。我们还设计了一种独特的铲取机制，该机制可以通过往复四杆连杆机构检索多达篮球大小的物体。此外，所有软件，包括新颖的目标定位和探索算法，都是使用ROS（机器人操作系统）开发的，ROS是开源的，并已被机器人技术界广泛采用。我们介绍了系统，我们的关键技术贡献和实验结果。

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