SBIR Phase I: A robotic system for removal of invasive plant species

SBIR 第一阶段：用于清除入侵植物物种的机器人系统

• 批准号: 2126637
• 负责人: Nancy Marek
• 金额: $ 24.01万
• 依托单位: BIRDHABITATBOT LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126637&HistoricalAwards=false
• 关键词: SBIR; Phase; robotic; system; removal

The broader impact of this SBIR Phase I project is to improve habitat for native plants and wildlife by removing invasive plants with a mobile robot. Worldwide invasion by non-native plant species is a primary concern in forest ecosystem health and biodiversity. Within the highly fragmented landscape of the Connecticut forest, two invasive plants significantly contribute to the degradation of habitat: Japanese barberry and multiflora rose. Current methods to eradicate them are time-consuming, expensive, and ultimately ineffective. This project presents a novel method of detecting and removing Japanese barberry and multiflora rose. Deep learning technology enables additional use with other invasive species, increasing the system's value throughout natural resource management. Additionally, once programmed, the robots will be easily operated, making them usable for a variety of personnel. This SBIR phase I project will advance current robot prototypes being tested for weed detection and removal. There is currently no mobile robot designed and programmed to remove understory invasive shrub species in a deciduous forest ecosystem. The technical challenges that will be addressed in building a feasible robot prototype include navigating over the unstructured terrain of the forest floor, developing a cutting attachment for the robotic arm, and creating a hybrid soft-rigid platform to withstand forest floor hazards while averting tree seedling damage. The proposed system will operate as follows: (1) an unpiloted aerial system (UAS, i.e., drone) flies over the canopy to capture images of the forest understory; (2) images are then labeled and converted to species location maps; and (3) utilizing these maps, a semi-autonomous mobile robot navigates to the invasive species locations for removal. The robots will be programmed for semi-automated missions monitored by an operator nearby at a safe distance to ensure worker safety.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.

SBIR第一阶段项目的更广泛影响是通过使用移动的机器人清除入侵植物来改善本地植物和野生动物的栖息地。非本地植物物种在世界范围内的入侵是森林生态系统健康和生物多样性的主要问题。在康涅狄格州森林的高度分散的景观，两种入侵植物显着有助于栖息地的退化：日本小檗和多花玫瑰。目前根除它们的方法耗时、昂贵，而且最终无效。本研究提出一种新的方法来检测和去除日本小檗和野蔷薇。深度学习技术可以与其他入侵物种一起使用，从而提高系统在整个自然资源管理中的价值。此外，一旦编程，机器人将很容易操作，使它们可用于各种人员。这个SBIR第一阶段项目将推进目前正在测试的杂草检测和清除机器人原型。目前还没有移动的机器人设计和编程，以消除落叶林生态系统中的林下入侵灌木物种。在构建可行的机器人原型时将解决的技术挑战包括在森林地面的非结构化地形上导航，为机器人手臂开发切割附件，以及创建一个混合的软硬平台以承受森林地面危害，同时避免树苗受损。拟议的系统将按以下方式运行：（1）无人驾驶航空系统（UAS，即，无人机）飞过树冠层以捕获森林林下植被的图像;（2）然后将图像标记并转换为物种位置地图;以及（3）利用这些地图，半自主的移动的机器人导航到入侵物种位置以进行移除。机器人将被编程为半自动任务，由附近的操作员在安全距离内进行监控，以确保工人的安全。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。