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Evaluation of Navigation in Mobile Robots for Long-Term Autonomy in Automotive Manufacturing Environments

机译:汽车制造环境长期自主导航评估

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In recent times, a number of reference implementations of Simultaneous Localization and Mapping (SLAM) and navigation techniques have been made publicly available via the ROS Community. Several implementations have transitioned to commercial products (vacuum robots, drones, warehouse robots, etc.). However, in such cases, they are specialized and optimized for their specific domains of deployment. In particular, their success criteria have been based primarily on mission completion and safety of humans around them. In this light, deployment in any new operational design domain (ODD) requires at least a careful verification of performance and often re-optimization. We seek the technological gaps that need to be addressed to ensure the mobile robots are fit for automotive manufacturing environments. Automotive final assembly environments pose significant additional challenges for mobile robot deployment. They are replete with relatively unstructured tasks with significant uncertainty, involve tasks with skills that require robots to work in collaboration with humans and are time sensitive. Currently, metrics for evaluating mobile robot functionalities have been based on accuracy, functionality and resource consumption. In addition to these, automotive assembly also requires consistency in execution times. This work evaluates the navigational capabilities of mobile robots in environments with static objects for time consistency as required by an automotive assembly process. The evaluation uses ASTM F3244-17 standard test method. It is performed on a simulated robot in Gazebo environment and Clearpath OTTO1500 robot in a laboratory environment.
机译:最近,已经通过ROS社区公开可获得同时定位和映射(SLAM)和导航技术的许多参考实现。几种实现转向商业产品(真空机器人,无人机,仓库机器人等)。但是,在这种情况下,它们专门用于其特定部署领域。特别是,他们的成功标准主要基于各周围的人类完成和安全性。在这种光中,任何新的操作设计域(奇数)中的部署都需要仔细验证性能并经常重新优化。我们寻求需要解决的技术差距,以确保移动机器人适合汽车制造环境。汽车最终装配环境对移动机器人部署构成了重要的额外挑战。它们与具有显着不确定性的相对非结构化的任务进行了复制,涉及具有需要机器人与人类合作工作的技能的任务,并且是时令敏感的。目前,用于评估移动机器人功能的指标基于精度,功能和资源消耗。除此之外,汽车组件还需要执行时间的一致性。根据汽车装配过程的要求,这项工作在具有静态对象的环境中的移动机器人的导航能力。评估使用ASTM F3244-17标准测试方法。它是在凉亭环境中的模拟机器人上进行,在实验室环境中的ClearPath Otto1500机器人。

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