SBIR Phase II: Robotic Forest Inventory and Mapping

SBIR 第二阶段：机器人森林清查和测绘

• 批准号: 2222426
• 负责人: Steven Chen
• 金额: $ 100万
• 依托单位: TREESWIFT INC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222426&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Robotic; Forest

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will create a sustainable business model for a new approach towards forest mensuration and monitoring. The core commercialization strategy is that multi-Unmanned Aerial Vehicle (UAV) teams replace the tape measure, not the forester. This technology addresses the needs for forest measurement techniques that are more accurate, efficient, and verifiable. The advances are critical for climate-smart forest management and carbon markets. There is a global environmental need for the assessment of trees and forest conditions, since trees are the lungs of the world as nature’s carbon sequestration engines. This technology is also addressing labor shortage challenges by making foresters more productive and providing opportunities and education in Science, Technology, Engineering, and Math (STEM) for rural workforces. The results of this project will be a foundation for capturing data of the natural world to accelerate growth of nature-based solutions. This Small Business Innovation Research (SBIR) Phase II project will advance knowledge in the fields of robotics and forestry. Improving forest measurement techniques is a generational challenge as lack of forest management, wildfires, natural disasters, and diminishing biodiversity are negatively affecting the broader economy and society. However, the current standard for measuring forest volumes is a manual human measurement, and there is a need for a more scalable solution. Robotics is the key to that solution, but there are still deep technical advances necessary to bring it to market. The first anticipated outcome is the development of algorithms, software, and hardware for intelligent, decentralized, autonomous multi-UAV systems that can coordinate in a dense forest. The second anticipated outcome is new forest sampling and tree volume techniques that leverage the ability to measure vast quantities of trees in precise detail, relative to manual measurements. The first outcome expands the frontier in the field of robotics and the second outcome expands the fields of forestry and ecology.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.

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力将为森林测定和监测的新方法创造一个可持续的商业模式。 核心商业化战略是多无人机（UAV）团队取代卷尺，而不是森林测量仪。这项技术满足了对更准确、更有效和更可核查的森林测量技术的需求。这些进展对于气候智能型森林管理和碳市场至关重要。全球环境需要对树木和森林状况进行评估，因为树木是世界之肺，是大自然的碳固存引擎。这项技术还通过提高林业工作者的生产力，为农村劳动力提供科学、技术、工程和数学（STEM）方面的机会和教育，解决了劳动力短缺的挑战。该项目的成果将成为获取自然世界数据的基础，以加速基于自然的解决方案的发展。这个小企业创新研究（SBIR）第二阶段项目将推进机器人和林业领域的知识。改进森林测量技术是一项世代挑战，因为缺乏森林管理、野火、自然灾害和生物多样性减少正在对更广泛的经济和社会产生负面影响。然而，目前测量森林蓄积量的标准是人工测量，需要一种更具可扩展性的解决方案。机器人技术是这一解决方案的关键，但要将其推向市场，仍然需要深入的技术进步。第一个预期成果是为智能、分散、自主的多无人机系统开发算法、软件和硬件，这些系统可以在茂密的森林中进行协调。第二个预期成果是新的森林取样和树木蓄积量技术，相对于人工测量，这些技术能够利用精确测量大量树木的能力。第一项成果扩展了机器人领域的前沿，第二项成果扩展了林业和生态学领域。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。