| Authors | TreaModelr et al. |
| Country | Forest Growers Research Ltd (Rotorua) and University of Canterbury (Chch) |
| Paper (PDF) | View paper |
| Category | Harvesting & Extraction › Remote sensing & mapping |
Autonomous forwareder; main focus on GPS based navigation
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An autonomous extraction machine is a self-driving or remotely supervised forest harvesting vehicle designed to transport felled logs from the cutting area to a roadside landing or processing point without continuous human operator input. The concept addresses one of the most physically demanding and accident-prone tasks in timber harvesting — the operation of skidders, forwarders, and cable yarding systems in steep, uneven, and debris-strewn terrain. Prototype systems developed by research groups and original equipment manufacturers typically combine GPS/GNSS localisation, inertial measurement units, stereo cameras, and LiDAR to build a real-time 3D model of the extraction corridor, enabling the machine to follow pre-planned routes, avoid obstacles such as slash piles and water crossings, and self-correct when terrain conditions deviate from pre-mission surveys. John Deere's autonomous forwarder research, Ponsse's intelligent machine programmes, and various university-industry collaborations in Scandinavia and North America have produced working prototypes capable of repeated strip-road extraction with minimal intervention. In the Australian context, Caterpillar and Tigercat equipment modified with autonomous kits have been evaluated in plantation settings in the Green Triangle and south-east Queensland, where consistent terrain and clearly defined extraction roads make the transition to autonomy more tractable. Key engineering challenges include load sensing for gripper optimisation when picking up log bunches, reliable perception under rain and dust, and fail-safe behaviour on slopes exceeding safe tipping angles. Commercial deployment is expected to progress from teleoperation-assisted modes through to supervised autonomy as sensor reliability and regulatory frameworks for uncrewed heavy machinery in Australian forests mature.
| Field | Value |
|---|---|
| Company | TreaModelr et al. |
| Country | Forest Growers Research Ltd (Rotorua) and University of Canterbury (Chch) |
| Type | Paper |
| Year | 2018 |
| FWPA RD&E | 4.4 |