STTR Phase I: Integrating Vision-Guided Collaborative Robots for Postharvest Processing of Produce

这个小企业技术转让（STTR）第一阶段项目的更广泛影响是使收获水果和蔬菜的加工者能够灵活地使用机器人自动化来满足他们的劳动力需求。自动化使用由计算机视觉引导的协作机器人（cobots），这些机器人在人类周围可能是安全的。该技术将有助于确保一致的产品质量和加工速度。通过一个强大的基于协作机器人的解决方案，该项目将为各种规模的农场提供负担得起的，可持续的和安全的手段，以跟上他们的生产目标，这将维持竞争和国家的粮食供应。该项目的另一个好处是，通过为更多技术导向的职位创造机会，提高农场工人的技能，包括监控和维护协作机器人。该项目引入了一种新的方法，通过从摄像头记录中学习，将人类执行的任务翻译给协作机器人，而不是为每次使用而繁琐地编程。它还将提高人们对协作机器人如何在共享工作空间中与人类一起安全使用的理解。这个小企业技术转让（STTR）第一阶段项目旨在使协作机器人与人类工人一起完成超越传统拾取和放置的任务成为可能。该技术将自动处理需要计算机视觉的生产线任务，这具有挑战性，因为准确和可靠的感知必须引导机器人的运动。研究已经将通往可行商业产品的道路上的技术挑战结合在一起，分为五个步骤。这些从任务域的正式描述开始，然后使用抗噪机器学习算法的鲁棒实现来自动学习任务，最后以将学习到的任务行为与视觉引导的协作机器人系统集成的解决方案结束。第一阶段将支持解决两个问题的研究。第一个是设计一种直观的方式来引出客户端任务域的精确规范。数字会话助理将利用多种方式进行启发。第二个是现有的实现无法生成协作感知和高效的协作机器人运动。该研究将调查和开发对协作机器人运动的重大改进，以提高同事的安全性，同时将处理时间减少预期的50%。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Technology Transfer (STTR) Phase I project is to empower the processors of harvested fruits and vegetables with the flexibility to use robotic automation to meet their labor needs. The automation uses collaborative robots (cobots) guided by computer vision, which are potentially safe around humans. The technology will help assure consistent produce quality and processing rates. Through a robust cobot-based solution, the project will provide an affordable, sustainable, and safe means for farms of all sizes to keep up with their production goals, which will sustain competition and the nation’s food supply. This project has the added benefit of upskilling workers in farms by creating openings for more technically oriented positions, both in monitoring and maintaining the cobots. Instead of tediously programming the cobot for each use, the project is introducing a new way of translating the tasks performed by humans to the cobot by learning from camera recordings. It will also improve understanding of how cobots can safely be used alongside humans in a shared working space.This Small Business Technology Transfer (STTR) Phase 1 project aims to make it possible to use cobots with human workers on tasks that go beyond the traditional pick-and-place. The proposed technology will automate processing line tasks that require computer vision, which is challenging because accurate and reliable perception must guide the robot’s motion. Research has coalesced the technical challenges on the path to a viable commercial product around five steps. These start with a formal description of the task domain followed by using robust implementations of noise-tolerant machine learning algorithms for automatically learning the task, and end with a solution that integrates the learned task behavior with a vision-guided cobot system. Phase 1 will support research toward addressing two problems. The first is to design an intuitive way to elicit a precise specification of the client’s task domain. A digital conversational assistant will utilize multiple modalities for the elicitation. The second is the inability of available implementations to generate coworker-aware and efficient cobot movements. The research will investigate and develop significant improvements to the cobot motion to improve coworker safety while reducing the processing time by an expected 50%.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.