Log Loading Automation for Timber-Harvesting Industry
Bibliographic record
Abstract
The timber-harvesting industry is lagging its peer industries, such as mining and agriculture, with respect to deployment of robotic, AI and autonomous technologies. In this paper, we tackle automation of a critical task that arises in transporting logs from the forest to the sawmill: the log loading operation. This work is motivated by the acute shortages of human operators and the need to improve the efficiencies of timber-harvesting processes. To this end, we demonstrate the full autonomy pipeline for the log loading operation with a fixed-base manipulator (a.k.a., the crane), starting with perception of logs around the machine, then grasp planning for where to grasp logs, through motion planning and control of the log loading maneuver. Our main contribution is in the full integration of the necessary elements to achieve a completely autonomous loading cycle, where the crane picks up and loads all logs within its reach on a trailer. Notable features of our implementation are a generalizable perception stack, a grasp planner to pick up multiple logs at a time and an extensive experimental campaign conducted outdoors, on a commercial log loader retrofitted for autonomy. Our results demonstrate an overall 87% success rate of the log loading operation, with primary failure cases due to log segmentation errors and deficiencies in the final height adjustment algorithm for grasping logs. We also present detailed timing results of the main parts of the autonomy pipeline, which support the feasibility of deployment in operational environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".