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Forwarder trafficability is a function of terrain and vehicle properties. Predicting trafficability is vital for energy efficient planning- and operator-assisting systems, as well as for remote and autonomous driving. Inaccurate or insufficient information can lead to inefficient paths, excessive fuel usage, equipment wear, and soil damages. Training trafficability models require data in a quantity hard to collect solely from in-field experiments, especially considering the need for data from situations ranging from very easy to non-traversable.

To circumvent this problem, we perform in-field system identification for a forwarder in the Nordic cut-to-length system, to obtain a calibrated multi-body dynamics simulation model traversing firm but potentially rough and blocky terrain. By letting the real-world forward derdrive in very difficult terrain, the model is able to reflect a wide range of real conditions. The model is used in simulations, where collecting large amounts of data from a variety of situations is easy, cheap, and hazard free. Using this data, a deep neural network is trained to predict trafficability in terms of attainable driving speed, energy consumption, and machine wear.

The resulting predictor model uses laser scanned terrains to efficiently produce trafficability measures with high fidelity and accuracy, e.g., depending on the vehicle’s precise location, speed, heading, and weight. Trafficability on wet and weak soil is not addressed in this work. The predictor model is machine specific, but general enough for practical application in diverse terrain conditions. Our emphasis on energy consumption enables elaborate calculations of emissions, profoundly contributing to sustainable forest operations. Apart from the benefits from reduced emissions, the model can also be used to optimize extraction trail routing, which is a major contributor to the total extraction cost. Rough terrain trafficability is only part of an optima loute, but it has been neglected in previous research. We see big potential in combining our predictor model with existing route optimization methods to achieve a more complete result. By creating an open library of annotated machine data and code for preparing input terrain-data and running the trafficability model, we enable adoption of the results by others and application in existing and new software.

We create partially annotated datasets from field measurements for developing models and algorithms for perception and control of forest machines using artificial intelligence, simulation, and experiments on physical testbeds.  The datasets, algorithms, and trained models for object identification, 3D perception, and motion planning and control will be made publicly available through data and code-sharing repositories.

The data is recorded using forest machines and other equipment with suitable sensors operating in the forest environment. The data include the machine and crane tip position at high resolution, and event time logs (StanForD) while the vehicle operates in high-resolution laser-scanned forest areas.  For annotation, the plan is to use both CAN-bus data and audiovisual data from operators that are willing to participate in the research. Also, by fusing visual perception with operator tree characteristics input or decision, we aim to develop a method for auto-annotation, facilitating a rapid increase in labeled training data for computer vision. In other activities, images of tree plants and bark are collected.

Research questions include, how to automate the process of creating annotated datasets and train models for identifying and positioning forestry objects, such as plants, tree species, logs, terrain obstacles, and do 3D reconstruction for motion planning and control? How large and varied datasets are required for the models to handle the variability in forests, weather, light conditions, etc.? Would additional synthetic data increase model inference accuracy?

In part we focus on forwarders traversing terrain, avoiding obstacles, and loading or unloading logs, with consideration for efficiency, safety, and environmental impact. We explore how to auto-generate and calibrate forestry machine simulators and automation scenario descriptions using the data recorded in the field. The demonstrated automation solutions serve as proofs-of-concept and references, important for developing commercial prototypes and for understanding what future research should focus on.

Extraction of timber is expensive, energy intensive, and potentially damaging to the forest soil. Machine development aims to mitigate risks for environmental impact and decrease energy consumption while maintaining or increasing cost efficiency. Development of rubber-tracked forwarders have gained renewed interest, partly due to climate change leading to unreliable weather, and the urgency of reducing emissions. The increased cost of rubber-tracks compared to wheels are believed to be compensated by higher driving speeds and larger payloads. Thus, the aim of this study was to theoretically investigate how productivity and cost efficiency of rubber-tracked forwarders can exceed that of wheeled equivalents. The calculations were made with fixed parameters, to evaluate performance in different conditions, and with parameters from 2 500 final felling stands in central Sweden, to evaluate performance in varied working conditions. Scenarios were compared to a baseline corresponding to mid-sized wheeled forwarders. The results show higher productivity with the increased driving speed and load weight enabled by rubber-tracks at all extraction distances, with larger differences at long extraction distances. Assuming 15% higher machine price for the rubber-tracked forwarder, increased speed and load weight lead to 40% cost reduction for 400 meters extraction distance. Furthermore, a rubber-tracked forwarder is likely to give access to a larger part of the harvest areas during longer seasons. The year-round accessible volumes are estimated to increase from 9% to 92% with a rubber-tracked forwarder. With rubber-tracks, good accessibility can be combined with low soil impact in a favourable way for both industry and ecosystem.

The Technodiversity project addresses technological diversity by gathering a common basis of technological knowledge and increasing the sensitivity for diversity in forest engineering. It aims to bring together and make generally available the existing knowledge in forest operations that is scattered across various European countries. It will serve as a bridge between different regions of Europe and generations of students, practitioners, scientists and academics. In this article, a small part of the e-learning module (https://technodiversity-moodle.ibe.cnr.it/) is presented in a glossary of some of the terms of forest operations.

Fully autonomous forwarding is a challenge, with more imminent scenarios including operator assistance, remote-controlled machines, and semi-autonomous functions. We present several subsystems for autonomous forwarding, developed using machine learning and physics simulation,

- trafficability analysis and path planning,

- autonomous driving,

- identification of logs and high quality grasp poses, and

- crane control from snapshot camera data.

Forwarding is an energy demanding process, and repeated passages with heavy equipment can damage the soil. To avoid damage and ensure efficient use of energy, it is important with a good path planning, adapted speed, and efficient loading and unloading of logs. The collection and availability of large amounts of data is increasing in the field of forestry, opening up for autonomous solutions and efficiency improvements. This is a difficult problem though, as the forest terrain is rough, and as weather, season, obstructions, and wear present challenges in collecting and interpreting sensor-data.

Our proposed subsystems assume access to pre-scanned, high-resolution elevation maps and snapshots of log piles, captured in between crane cycles by an onboard camera. By utilizing snapshots instead of a continuous image stream in the loading task, we separate image segmentation from crane control. This removes any coupling to specific vehicle models, and greatly increases the limit on computational resources and time for the challenge of image segmentation. Log piles are normally static except at the grasp moments and given good enough grasp poses, this lack of information is not necessarily a problem.

We show how snapshot image data can be used when deploying a Reinforcement Learning agent to control the crane to grasp logs in challenging piles. Given pile RGB-D images, our grasp detection model identifies high quality grasp poses, allowing for multiple logs to be loaded in each crane cycle. Further, we show that our model is able to learn to avoid obstructions in the environment such as tree stumps or boulders. We discuss the possibility of using our model to optimize the loading task over a sequence of grasps.

Finally, we discuss how the solutions can be combined in a multi-agent forwarding system with or without a human operator in-the loop.

For customer-oriented wood supply, buffering is required for flexibility to handle interactions in the wood procurement system. This includes balancing lead-time and operational cost by using stocks and production capacity as buffers. Despite the well-known challenge to balance the interactions between harvesting and forwarding in Nordic mechaniced CTL-operations, there has been limited research on how the machine groups can be staffed to enable flexibility and more focus on other measures to create flexibility. Therefore, this study explored trade-offs between wood lead-time and harvesting cost in the stump-to-roadside part of the wood supply chain by altering the numer of full-time working operators in the harvesting groups. This was done using discrete-event simulations implemented in Anylogic software. Input data included information about operational conditions in 1500 forest stands. The results revealed that the best balance was to have sufficient harvesting capacity to adjust wood lead times at the expense of increased harvesting costs. Of the tested options, the best balance was achieved when staffing a two-machine group with three operators, and thereby allocating 50% of the used work-shifts to regulating the field wood stock between the two machines. This resulted in the shortest lead times and the smallest harvesting cost increase. Compared to the option with no flexibility for stock adjustment (4 operators), the average lead-time could be reduced to one tenth at a cost increase of 3.4%. These findings have the potential to improve decisions of how harvesting groups are staffed to balance specific objectives of desired lead times and costs, which migh prove to be a valuable addition to the already used measures to manage wood flow.

Within cut-to-length forwarding, a theoretical semi-autonomous teleoperation concept called tele-extraction, with automation of crane work and teleoperation of driving, was modeled and simulated. Both configurations modeled had greater potential for cost reduction than a previously studied alternative where the driving was automated, and crane work was teleoperated. Teledrive with teleoperated driving empty, driving loaded, and driving between log piles while loading, showed a reduced cost of 10% for five operators on ten forwarders, whereas teledrive with both loading and driving while loading automated showed a reduced cost of 18% at four operators. In both configurations, the lowest cost was reached at about 10% lower productivity compared to standard forwarding. Increased extraction distance had a negative impact on potential for cost reduction since the driving was teleoperated while terminal activities were autonomous.

Traversability is of major importance in forestry, where heavy vehicles, weighing up to 40 tons whenfully loaded, traverse rough and sometimes soft terrain. Forest remote sensing is becoming available atresolutions where surface roughness and slope can be determined at length-scales smaller than the forestmachines. Using 3D multibody dynamics simulation of a forest machine driving in virtual terrain replications, the interaction can be captured in great detail. The observed traversability is then automaticallya function of the vehicle geometry, dynamics, and of the local terrain topography relative to heading. Weexpress traversability with three complementary measures: i) the ability to traverse the terrain at a target speed, ii) energy consumption, and iii) machine body acceleration. For high traversability, the lattertwo should be as small as possible while the first measure is at maximum. The simulations are, however,too slow for systematically probing the traversability over large areas. Instead, a deep neural networkis trained to predict the traversability measures from the local heightmap and target speed. The trainingdata comes from simulations of an articulated vehicle with wheeled bogie suspensions driving over procedurally generated terrains while observing the dynamics and local terrain topology. We evaluate themodel on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90% on terrains with 0.25 m resolution and it is 3000 times faster than the groundtruth realtime simulation and trivially parallelizable, making it well suited for traversability analysis andoptimal route planning over large areas. The trained model depends on the vehicle heading, target speed,and detailed features in the topography that a model based only on local slope and roughness cannotcapture. We explore traversability statistics over large areas of laser-scanned terrains and discuss howthe model can be used as a complement or in place of the currently used terrain classification scheme.