Imagine having a digital twin of every single tree in a forest. Instead of people walking through the woods to guess which trees to cut, a team of robot dogs and drones maps the area in 3D. This data then tells a smart harvester exactly which tree to remove to keep the forest healthy and maximize wood production.
Current forest management lacks tree-level data, leading to uncertainty in timber yields and inefficient thinning operations. This prevents the industry from maximizing carbon sequestration and sustainable wood production.
A robotic ecosystem including legged robots (ANYmal), drones, and a semi-automated lightweight harvester. They developed 3D spatial mapping and AI for semantic tree labeling.
If you are a timber firm dealing with the uncertainty of when thinning is needed—this project developed a robotic mapping system that provides tree-level data. This allows for precise predictions of timber yields and better planning of equipment deployment.
If you are a carbon consultant dealing with low-accuracy forest biomass estimates—this project developed 3D spatial representations that measure crown volume and tree diameters. This improves the granularity of carbon credit schemes and carbon farming policy.
If you are a machinery maker dealing with the high cost of manual logging—this project developed a semi-automated lightweight harvester for selective logging. This moves the industry toward full process automation.
Based on available project data, specific pricing or cost structures are not provided as the project is currently in the development and trial phase.
The project aims for large-scale precision forestry management using a team of heterogeneous robots, with commercial pathways identified through engagement with industrial companies.
Based on available project data, there are no specific details regarding IP or licensing agreements provided in the summary.
The project runs from 2022-09-01 to 2026-02-28, with the current phase focusing on semi-automating a lightweight harvester.
The system integrates 3D spatial data with a semi-autonomous harvester, utilizing hardware upgrades for chassis balancing and hydraulic manipulator control.
The consortium is well-balanced for technology transfer, consisting of 10 partners across 7 countries. With a 30% industry ratio (3 industrial partners, including 1 SME) and 6 universities, the project bridges the gap between academic robotics research and commercial forestry application.
Contact the Rheinische Friedrich-Wilhelms-Universität Bonn
Contact us to connect with the DIGIFOREST industrial partners for early adoption.
