ERI: Fuel Reduction Aided by Multi-Robot Teams in Forests

This Engineering Research Initiation (ERI) award supports research that will contribute new knowledge in robotic removal of wood waste from forests, thereby promoting the progress of science, advancing the national health, prosperity and welfare. Forest fires cause severe damage to communities by destroying property and livelihoods. To mitigate wildfire risk and severity, several clearing methods are utilized, ranging from mechanical fuel reduction, to prescribed burning and to grazing by animals. For mechanical reduction, limbing - the removal of lower branches from trees — is one of the most critical methods for preventing crown fires, as these lower limbs act as "ladders" that help surface fires reach the canopy. However, limbing remains largely reliant on manual, hazardous and labor-intensive activities performed by forestry workers. This project look to provide needed knowledge for the development of robotic teams capable of autonomously cutting and catching lower tree limbs. The new robotic system looks to alleviate the workload of forestry workers in some of their most physically demanding activities, improving occupational health. It also intends to accelerate removal of hazardous fuels from the forest, contributing to wildfire prevention in the US. Results from this research intend to help protect US communities located near or within forests, as well as forest-based industries such as logging and tourism. The project will provide undergraduate and graduate students with essential knowledge in robotic wood waste removal and positively impact engineering education. This research aims to make fundamental contributions to establish a systematic framework for the design and control of cutting and catching robots for fuel reduction activities in forests. The research looks to achieve this goal by designing and optimizing novel rotary end effectors to perform robotic limbing on structurally integrated tree branches. Additionally, the research seeks to involve development of hierarchical control algorithms to plan, execute, and synchronize the motion of the cutting and catching robots. At the higher level, a centralized spiral-based planner looks to compute optimal trajectories for the cutter and catcher robots, considering tree geometry, kinematic and actuation constraints, and maximizing branch collection. At the lower level, each robot looks to operate under decentralized control, utilizing control barrier functions to closely follow the planned trajectory, while ensuring real-time adaptability to dynamic obstacles and environmental uncertainties. 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.