SBIR Phase I: Improving farmer safety and grain storage efficiencies via a remote-controlled grain management and extraction robot

这个小企业创新研究（SBIR）第一阶段项目的广泛影响/商业潜力旨在帮助全国各地的农民以远程和安全的方式提高他们的粮食储存效率。除了展示移动机器人的技术可行性外，该项目还可以帮助改进关键的设计考虑因素，如成本、可移植性和可用性，这将有助于在商业化期间促进广泛的客户采用。虽然该机器人最初是为美国普通储存的谷物设计的，但这项技术可能会应用于世界各地的许多与农业相关的商品。机器人技术有可能提高农民的安全，同时改善粮食管理实践，从而产生更高效、更稳定的粮食供应链。这个小企业创新研究（SBIR）第一阶段项目探讨了移动机器人在粮食储存评估中的应用。粮仓为机器人的操作提供了具有挑战性的环境，包括：极端灰尘和温度，移动流体动力学和粮食质量挑战。研究重点包括对影响机器人效率的关键谷物表面变量进行分类和表征，根据频率和重要性确定前3-5个谷物管理任务，分析谷物特性对每个任务的影响，并设计和实施受控环境研究以量化性能需求。该项目还将为顶级用例任务识别和定义潜在的谷物接触路径/模式，并分析机器人在谷物仓内操作的潜在故障模式。一旦完成，该研究可能会证明对收获后工作流程的影响，开发自主操作，并建立一个用于粮仓的安全机器人平台。该奖项反映了美国国家科学基金会的法定使命，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/commercial potential of this Small Business Innovation Research (SBIR) Phase I project seeks to help farmers across the nation improve their grain storage efficiencies in a remote and safe manner. In addition to demonstrating technical feasibility of a mobile robot, this project may help refine key design considerations such as cost, portability, and usability, that will help promote widespread customer adoption during commercialization. Though the robot will be initially designed for commonly stored cereal grains in the U.S., this technology may be applied across numerous agriculture-related commodities around the world. The robotics technology has the potential to enhance farmer safety while simultaneously improving grain management practices that yield more efficient and stable food supply chains. This Small Business Innovation Research (SBIR) Phase I project explores the use of mobile robots for grain storage assessment. Grain bins provide challenging environments for the operation of robots including: dust and temperature extremes, shifting fluid dynamics, and grain quality challenges. The focus of the research includes classifying and characterizing key grain surface variables that impact robotic effectiveness, identifying the top 3-5 grain management tasks by frequency and importance, and analyzing the impacts of grain characteristics on each task, and designing and conducting controlled environmental studies to quantify performance requirements. The project will also identify and define potential grain engagement paths/patterns for top use case tasks, and analyze potential failure modes for robot operations within grain bins. Once complete, the research may demonstrate the impact on post-harvest workflows, develop autonomous operations, and build a safe robot platform for use in grain bins.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.