FORWARD: Dataset of a forwarder operating in rough terrain
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911531195695104 |
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| author | Lundbäck, Mikael Wallin, Erik Häggström, Carola Nyström, Mattias Grönlund, Andreas Richardson, Mats Jönsson, Petrus Arnvik, William Hedström, Lucas Fälldin, Arvid Servin, Martin |
| author_facet | Lundbäck, Mikael Wallin, Erik Häggström, Carola Nyström, Mattias Grönlund, Andreas Richardson, Mats Jönsson, Petrus Arnvik, William Hedström, Lucas Fälldin, Arvid Servin, Martin |
| contents | We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The forwarder was additionally equipped with cameras, operator vibration sensors, and multiple IMUs. The data includes event time logs recorded at 5 Hz of driving speed, fuel consumption, machine position with centimeter accuracy, and crane use while the forwarder operates in forest areas, aerially laser-scanned with a resolution of around 1500 points per square meter. Production log files (Stanford standard) with time-stamped machine events, extensive video material, and terrain data in various formats are included as well. About 18 hours of regular wood extraction work during three days is annotated from 360-video material into individual work elements and included in the dataset. We also include scenario specifications of conducted experiments on forest roads and in terrain. Scenarios include repeatedly driving the same routes with and without steel tracks, different load weights, and different target driving speeds. The dataset is intended for developing models and algorithms for trafficability, perception, and autonomous control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. In part, we focus on forwarders traversing terrain, avoiding or handling obstacles, and loading or unloading logs, with consideration for efficiency, fuel consumption, safety, and environmental impact. Other benefits of the open dataset include the ability to explore auto-generation and calibration of forestry machine simulators and automation scenario descriptions using the data recorded in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17318 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FORWARD: Dataset of a forwarder operating in rough terrain Lundbäck, Mikael Wallin, Erik Häggström, Carola Nyström, Mattias Grönlund, Andreas Richardson, Mats Jönsson, Petrus Arnvik, William Hedström, Lucas Fälldin, Arvid Servin, Martin Robotics Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Applied Physics We present FORWARD, a high-resolution multimodal dataset of a cut-to-length forwarder operating in rough terrain on two harvest sites in the middle part of Sweden. The forwarder is a large Komatsu model equipped with vehicle telematics sensors, including global positioning via satellite navigation, movement sensors, accelerometers, and engine sensors. The forwarder was additionally equipped with cameras, operator vibration sensors, and multiple IMUs. The data includes event time logs recorded at 5 Hz of driving speed, fuel consumption, machine position with centimeter accuracy, and crane use while the forwarder operates in forest areas, aerially laser-scanned with a resolution of around 1500 points per square meter. Production log files (Stanford standard) with time-stamped machine events, extensive video material, and terrain data in various formats are included as well. About 18 hours of regular wood extraction work during three days is annotated from 360-video material into individual work elements and included in the dataset. We also include scenario specifications of conducted experiments on forest roads and in terrain. Scenarios include repeatedly driving the same routes with and without steel tracks, different load weights, and different target driving speeds. The dataset is intended for developing models and algorithms for trafficability, perception, and autonomous control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds. In part, we focus on forwarders traversing terrain, avoiding or handling obstacles, and loading or unloading logs, with consideration for efficiency, fuel consumption, safety, and environmental impact. Other benefits of the open dataset include the ability to explore auto-generation and calibration of forestry machine simulators and automation scenario descriptions using the data recorded in the field. |
| title | FORWARD: Dataset of a forwarder operating in rough terrain |
| topic | Robotics Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2511.17318 |