SBIR Phase I: Robotic Forest Inventory and Mapping

SBIR 第一阶段：机器人森林清查和测绘

• 批准号: 1938565
• 负责人: Steven Chen
• 金额: $ 22.5万
• 依托单位: TREESWIFT INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938565&HistoricalAwards=false
• 关键词: SBIR; Phase; Robotic; Forest; Inventory

The broader impact/commercial potential of this Small Business Innovation (SBIR) Phase I project is modernizing monitoring systems for the forestry industry, environmental sustainability, and nature conservation purposes. The estimated commercial potential on the $325 B global forestry industry is an additional $3.4 B of unlocked value through faster, cheaper, and more accurate inventory systems. Warmer temperatures have contributed to an explosion in pest epidemics that have destroyed over 120 million acres of timberland in the US since 1998. In addition, these temperatures and dead trees have exacerbated wildland fire, with annual economic damage estimated to be $350 B. The societal impact of these problems is pervasive, as smoke plumes can drift for thousands of miles and adversely affect human health and environmental pollution. One fundamental requirement to address these problems is an automated forest monitoring system, as current systems still heavily rely on manual measurements. This project will enhance scientific and technological understanding by developing autonomous and large-scale semantic mapping robotic systems for dense, natural forests to tackle these broader high-impact problems. In addition, this project will provide high-tech career opportunities in rural communities by training skilled operators to develop, deploy, and control the robot teams in forests. This Small Business Innovation (SBIR) Phase I project will develop the first commercially viable automated timber cruise to estimate forest volume from below the canopy level. The forestry industry still relies on manual measurements because, due to fundamental technical challenges, the technology to autonomously measure tree sizes under the canopy over long distances does not exist. This project will overcome two limiting challenges: 1) Robust Autonomy Challenge: No one has achieved robust autonomy in truly 3D, unstructured, GPS-denied environments where manual control (teleoperation) is not feasible; and 2) Large-Scale Semantic Mapping Challenge: No one has attempted semantic mapping at the scale and accuracy proposed, as most demonstrations have been for a few object instantiations and without the need for precise measurement. To tackle these challenges, the project anticipates three technical results: 1) Real-time tree detection to robustly detect trees in challenging conditions; 2) Continuous-Time Semantic Simultaneous Localization and Mapping to precisely model trees over vast distances; and 3) Fast Online Motion Planning with Deep Model Predictive Control to robustly navigate unmanned aerial vehicles in cluttered forest environments.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（SBIR）第一阶段项目的更广泛的影响/商业潜力是现代化的林业监测系统，环境可持续性和自然保护的目的。通过更快、更便宜和更准确的库存系统，全球林业产业的估计商业潜力为3250 B美元，另外还有34 B美元的解锁价值。自1998年以来，气温升高导致了害虫流行的爆发，摧毁了美国超过1.2亿英亩的廷伯兰。此外，这些温度和死亡的树木加剧了野火，每年的经济损失估计为350 B美元。这些问题的社会影响是普遍的，因为烟羽可以漂移数千英里，对人类健康和环境污染产生不利影响。解决这些问题的一个基本要求是自动化森林监测系统，因为目前的系统仍然严重依赖人工测量。该项目将通过为茂密的自然森林开发自主和大规模语义绘图机器人系统来加强科学和技术理解，以解决这些更广泛的高影响问题。此外，该项目还将通过培训熟练的操作员来开发、部署和控制森林中的机器人团队，为农村社区提供高科技职业机会。这个小型企业创新（SBIR）第一阶段项目将开发第一个商业上可行的自动木材巡航，以估计林冠层以下的森林体积。林业仍然依赖人工测量，因为由于基本的技术挑战，长距离自主测量树冠下树木尺寸的技术还不存在。该项目将克服两个限制性挑战：1）强大的自主性挑战：没有人在真正的3D，非结构化，GPS拒绝的环境中实现强大的自主性，（遥操作）是不可行的;和2）大规模语义映射的挑战：没有人尝试过语义映射的规模和准确性，因为大多数演示都是针对少数对象实例的，并且不需要精确测量。为了应对这些挑战，该项目预计将取得三项技术成果：1）实时树木检测，以在具有挑战性的条件下稳健地检测树木; 2）连续时间语义同时定位和映射，以精确地对远距离树木进行建模;和3）的方法利用深度模型预测控制进行快速在线运动规划，在杂乱的森林环境中稳健地导航无人机。该奖项反映了NSF的法定基金会的使命是履行其使命，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价，被认为值得支持。