SBIR-STTR Award

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.

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.