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RaCOON

Financiado

Radar Classification Of Obstacles in Nature

Forests cover more than 40% of Europe’s surface and are essential for biodiversity, provide fresh water, absorb carbon and prevent erosion. Yet they face detrimental effects of climate change, such as wildfires or outbreaks of the... ver más

31/05/2026

ORU

223K€

Presupuesto del proyecto: 223K€

Líder del proyecto

OREBRO UNIVERSITY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-07-26

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

I+D

Cerrada hace 2 años

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1 Participantes

ORU

222.73K€ | Lider

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Duración del proyecto: 34 meses Fecha Inicio: 2023-07-26
Fecha Fin: 2026-05-31

Líder del proyecto

OREBRO UNIVERSITY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 223K€

Descripción del proyecto Forests cover more than 40% of Europe’s surface and are essential for biodiversity, provide fresh water, absorb carbon and prevent erosion. Yet they face detrimental effects of climate change, such as wildfires or outbreaks of the bark beetle. The field of robotics offers a pallet of tools to help manage and monitor forests, yet mainly by flying robots. Ground robots that could carry heavier equipment and last longer struggle in vegetation since their autonomy systems have been developed for obstacle-free scenarios (e.g., driving on roads). The research proposed here, Radar Classification Of Obstacles in Nature (RaCOON), aims to enable the deployment of ground robots in forests by giving them the ability to decide which vegetation can be safely driven through. The applicant will deploy a new sensor modality, i.e. radar, and develop a novel sensor fusion system that will classify vegetation into the obstacle and non-obstacle categories. This additional information will allow ground robots to autonomously plan trajectories and navigate in vegetation. The problem will be approached first by exploring the possibilities of radars in a proof-of-concept experiment. Then, a forest robotic dataset will be recorded in various types of vegetation. The experience from the proof-of-concept experiment and the recorded data will motivate the design of the final sensor fusion system. The outcomes of RaCOON will be 1) dissemination of the new system and dataset to the research community and professional networks, 2) training of the applicant in the deployment of radars for mobile robots and 3) extending the applicant’s professional network and independent research capabilities, advancing him towards starting his own robust field robotics research group.

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