Wildfires have intensified globally due to climate change and ineffective land management. Traditional prevention methods, such as manual clearing of firebreaks, are costly, labor-intensive, and hazardous. The Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SAFEFOREST) project addressed these issues by developing an integrated robotic solution combining a semiautonomous heavy-duty autonomous aerial vehicle (AGV) for surface fuel clearing, a multimodal autonomous aerial vehicle (AAV) for mapping, and an integrated forest management information system (FMIS). The AGV, a retrofitted compact track loader, leverages advanced perception and lidar-inertial odometry (LIO-SAM) for robust localization. The AAV, equipped with lidar and stereoscopic cameras, generates high-resolution 3D terrain and vegetation maps to optimize firebreak planning. The FMIS uses AAV data to produce fuel cluster maps and traversability analyses, improving autonomous AGV operations. Field validation in rugged environments demonstrated SAFEFOREST’s efficiency in mission planning, vegetation classification, and clearing. This article summarizes SAFEFOREST’s technological breakthroughs, key lessons from field trials, and potential impacts on sustainable forest management and wildfire risk mitigation.
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