首页|Research from University of Siena Yields New Study Findings on Robotics [One small step for a robot,one giant leap for habitat monitoring: A structural survey of EU forest habitats with Roboticallymounted Mobile Laser Scanning (RML S)]
Research from University of Siena Yields New Study Findings on Robotics [One small step for a robot,one giant leap for habitat monitoring: A structural survey of EU forest habitats with Roboticallymounted Mobile Laser Scanning (RML S)]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on robotics have been published. According to news reporting from Siena,Italy,by NewsRx journa lists,research stated,"EU States are mandated by the 92/43/EEC Habitats Direct ive to generate recurring reports on the conservation status and functionality o f habitats at the national level. This assessment is based on their floristic an d,especially for forest habitats,structural characterization." Our news reporters obtained a quote from the research from University of Siena: "Currently,habitat field monitoring efforts are carried out only by trained hum an operators. The H2020 Project ‘Natural Intelligence for Robotic Monitoring of Habitats - NI' aims to develop quadrupedal robots able to move autonomously in t he unstructured environment of forest habitats. In this work,we tested the loco motion performance,efficiency and the accuracy of a robot performing structural habitat monitoring,comparing it with traditional field survey methods inside s elected stands of Luzulo-Fagetum beech forests (9110 Annex I Habitat). We used a quadrupedal robot equipped with a Mobile Laser Scanning system (MLS),implement ing for the first time a structural monitoring of EU forest habitats with a Robo tically-mounted Mobile Laser Scanning (RMLS) platform. Two different scanning tr ajectories were used to automatically map individual tree locations and extract tree Diameter at Breast Height (DBH) from point clouds. Results were compared wi th field human measurements in terms of accuracy and efficiency of the survey. T he robot was able to successfully execute both scanning trajectories,for which we obtained a tree detection rate of 100 %. Circular scanning traje ctory performed better in terms of battery consumption,Root Mean Square Error ( RMSE) of the extracted DBH (2.43 cm or 10.73 %) and prediction powe r (R2adj = 0.72,p <0.001). The RMLS platform improved sur vey efficiency with 19.31 m2/min or 1.77 trees/min in comparison with 3.45 m2/mi n or 0.32 trees/min of traditional survey."
University of SienaSienaItalyEurop eEmerging TechnologiesMachine LearningRobotRoboticsRobots
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