Collective Perception in Swarm Robotics

自动化过去是，现在仍然是推动我们经济转型的动力。然而，直到今天，机器人仍然大多被归类到严格控制的工业装配线上，在那里，它们的所有动作都经过精心规划和调整，以完成非常具体和有限的任务。尽管科学家和工程师尽了最大努力，但机器人还没有准备好离开他们的监管环境，去野外和未知的地方漫步。我们一直在进行一项研究计划，将群体机器人技术转化为实际的日常应用。在过去的6年里，我们在连接维护、长期自主、模式形成、使用最少基础设施的协作定位、多机器人地图绘制、对象跟踪、人群界面、空间探索和协作运输方面做出了贡献。这些贡献是使用通过以前的RTI比赛获得的机器人实现的，这些机器人现在迫切需要升级和更换，以继续该计划。我们目前的重点是协作感知：对可靠、弹性和安全感知的需求是自主机器人的核心挑战。机器人学中的核心感知算法之一是同时定位和映射，或SLAM。SLAM提供的态势感知对于大多数机器人系统来说是必不可少的。虽然它可以在每个机器人上单独完成，但在与多个机器人组一起工作时，协作执行通常是有益的。在协作SLAM或C-SLAM中，每个单独的机器人执行单个机器人SLAM，然后与其他机器人进行通信和数据关联，以便在它们之间建立共同的全局地图。这样，他们可以共享一个共同的本地化参考框架，并相互了解决策环境。我们的总体目标是降低在未知和不可控环境中部署的多机器人定位和映射算法的失败率。我们要求购买一套20台Agilex Limo：一个小型但功能强大的机器人，配备了强大的车载计算机、3D摄像头、惯性组件和激光雷达。拥有至少20个机器人将使我们能够试验分级群、协作运输、能量充足和协作SLAM。我们还请求2个掩体微型机器人来支持我们对GPS拒绝环境的多机器人探索和地图绘制的研究。我们的研究成果将与欧洲航天局(ESA)、加拿大航天局(CSA)和NASA喷气推进实验室合作进行测试。在接下来的五年里，我们预计申请者实验室的6名博士后研究员、10名博士、8名硕士和15名本科生将使用这种设备进行培训。

Automation has been, and still is, the driving force behind the transformation of our economies. However, to this day robots are still mostly relegated to tightly controlled industrial assembly lines, where all their motions are carefully planned and tuned for very specific and limited tasks. Despite the best efforts of scientists and engineers, robots are not yet ready to leave their supervised settings to wander the wild and the unknown. We have been carrying a research program to make swarm robotics technology into practical, every day applications. In the past 6 years, we have produced contributions to connectivity maintenance, long-term autonomy, pattern formation, collaborative localization with minimal infrastructure, multi-robot mapping, object tracking, human-swarm interfaces, space exploration and collaborative transport. These contributions were made possible using robots obtained via previous RTI competitions, that are now in dire need of upgrade and replacement to continue the program. Our current focus is collaborative perception: the need for reliable, resilient and safe perception is a core challenge of autonomous robotics. One of the core perception algorithm in robotics is Simultaneous Localization And Mapping, or SLAM. Situational awareness, as provided by SLAM, is essential to most robotic systems. While it can be done on each robot individually, it is often beneficial to perform it collaboratively when working with groups of robots. In Collaborative SLAM, or C-SLAM, each individual robot performs single-robot SLAM, then communicates and performs data association with the other robots in order to build a common global map between them. This way, they can share a common reference frame for localization and a mutual understanding of the environment for decision-making. Our general objective is to reduce the rate of failures of multi-robot localization and mapping algorithms deployed in unknown and uncontrolled environments. We request a set of 20 AgileX Limo: a small but capable robot featuring a powerful onboard computer, a 3D camera, an inertial unit, and a lidar. Having at least 20 robots would allow us to experiment hierarchical swarms, collaborative transport, energy sufficiency, and collaborative SLAM. We also request 2 Bunker Mini robots to support our research on multi-robot exploration and mapping of GPS-denied environments. Our research output will be tested in collaboration with the European Space Agency (ESA), the Canadian Space Agency (CSA), and NASA JPL. Over the next five years, we expect 6 Postdoctoral fellows, 10 PhDs, 8 Master's and 15 undergraduates from the applicant's laboratory to be trained using this equipment.