I-Corps: Advancing Precision Manufacturing in Non-Factory Environments with Low-Cost Positioning Sensor

所提出的创新是一种适用于非工厂制造环境的鲁棒、低成本的定位和本地化系统。该系统将聚焦波束声纳传感器信息与本地平台的运动学/动力学模型估计相结合，以提供同时定位和映射（SLAM）信息。该系统非常适合于GPS不可用的制造，并且解决了基于激光雷达（激光干涉检测和测距）扫描或视觉的当前移动的机器人定位系统中的许多限制。 基于声学的系统在非工厂制造环境中更加鲁棒，但是由于与单个传感器相关联的宽扫描场而不常用于移动的机器人定位系统中。所提出的创新将提供专用于每个传感器的本地处理能力，以收集特征信息，然后可以将其纳入整体定位策略。拟议的设备将增强目前存在的机械化平台，具有基本定位能力，可以提高操作员的性能，并将开辟机器控制的新功能。如果成功，这项技术可以对提高非工厂制造环境中工人的生产力产生重大影响。从历史上看，这个行业几乎没有应用革命性的技术（工厂自动化等）。现场维护、制造和组装并没有看到他们的工厂表亲的生产率提高。现场工人的生产力较低，受伤较多，职业生涯较短。这项工作将提供创新，直接解决这些工人的生产力提高工具。此外，它还提高了这些行业机器人智能机器的许多相关发展的生产力。这些工具使非工厂制造业更安全，更高效，需要更高的技能，并促进了美国制造业的专业化。

The proposed innovation is for a robust, low-cost positioning and localization system that is suitable for use in non-factory manufacturing environments. The system combines focused-beam sonar sensor information with kinematic/dynamic model estimates of the local platform to provide simultaneous localization and mapping (SLAM) information. This system is well suited for manufacturing in which GPS is not available, and addresses many of the limitations in current mobile robot positioning systems based on Lidar (Laser Interferometry Detection and Ranging) scanning or vision. Acoustic-based systems are much more robust in non-factory manufacturing environments but are not commonly used in mobile robot positioning systems because of the wide scan field associated with a single sensor. The proposed innovation will provide local processing capability dedicated to each sensor to collect feature information that can then be incorporated into the overall localization strategy. The proposed device will enhance the mechanization platforms that currently exist with basic positioning capability allowing improved performance by the operator and will open up new capabilities for machine control.If successful, this technology can have a significant impact on improving the productivity of workers in non-factory manufacturing environments. Historically, this industry has seen little application of technology that has revolutionized many other industries (factory automation, etc.). Field maintenance, fabrication and assembly has not seen the productivity gains of their factory based cousins. Field workers are less productive, subject to more injuries and have shorter careers. This effort will provide innovations to directly address enhanced productivity tools for these workers. Further, it enhances the productivity of many related developments in robotics intelligent machines for these industries. These tools make non-factory manufacturing safer, more productive, require greater skills, and promote the profession of manufacturing in the US.