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Publications (10 of 66) Show all publications
Lundbäck, M., Lindroos, O. & Servin, M. (2024). Rubber-tracked forwarders: productivity and cost efficiency potentials. Forests, 15(2), Article ID 284.
Open this publication in new window or tab >>Rubber-tracked forwarders: productivity and cost efficiency potentials
2024 (English)In: Forests, ISSN 1999-4907, E-ISSN 1999-4907, Vol. 15, no 2, article id 284Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
timber extraction, soil impact, accessibility, machine prototype, CTL logging
National Category
Forest Science
Research subject
Systems Analysis
Identifiers
urn:nbn:se:umu:diva-211024 (URN)10.21203/rs.3.rs-3087217/v1 (DOI)001170371700001 ()2-s2.0-85185833101 (Scopus ID)
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, DIA 2017/14 #6
Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2024-03-12Bibliographically approved
Wallin, E., Wiberg, V. & Servin, M. (2023). Multi-log grasping using reinforcement learning and virtual visual servoing. Robotics, 13(1), Article ID 3.
Open this publication in new window or tab >>Multi-log grasping using reinforcement learning and virtual visual servoing
2023 (English)In: Robotics, E-ISSN 2218-6581, Vol. 13, no 1, article id 3Article in journal (Refereed) Published
Abstract [en]

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding in a simulated environment. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves various problems of dynamics and path planning, where understanding the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilise a virtual camera to provide an image stream from reconstructed 3D data. We use Cartesian control to simplify domain transfer to real-world applications. Because log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limits on computational resources and time for the challenge of image segmentation, and allows for data collection in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2–5 logs.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
autonomous forwarding, visual servoing, virtual camera, reinforcement learning, multi-log grasping, Cartesian control
National Category
Robotics Control Engineering Other Physics Topics Computer Vision and Robotics (Autonomous Systems)
Research subject
Physics; Automatic Control
Identifiers
urn:nbn:se:umu:diva-220402 (URN)10.3390/robotics13010003 (DOI)2-s2.0-85183350095 (Scopus ID)
Projects
Mistra Digital Forest
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, Grant DIA 2017/14 #6
Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-02-09Bibliographically approved
Wiberg, V., Wallin, E., Nordjell, T. & Servin, M. (2022). Control of rough terrain vehicles using deep reinforcement learning. IEEE Robotics and Automation Letters, 7(1), 390-397
Open this publication in new window or tab >>Control of rough terrain vehicles using deep reinforcement learning
2022 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 1, p. 390-397Article in journal (Refereed) Published
Abstract [en]

We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27 degrees, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Artificial Intelligence, Control and Optimization, Computer Science Applications, Computer Vision and Pattern Recognition, Mechanical Engineering, Human-Computer Interaction, Biomedical Engineering, Control and Systems Engineering
National Category
Robotics Computer Vision and Robotics (Autonomous Systems) Other Physics Topics
Research subject
Physics; Computer Science; Automatic Control
Identifiers
urn:nbn:se:umu:diva-189485 (URN)10.1109/lra.2021.3126904 (DOI)000721999500008 ()2-s2.0-85119416792 (Scopus ID)
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, 2017/14 #6eSSENCE - An eScience CollaborationSwedish National Infrastructure for Computing (SNIC), 2021/5-234
Available from: 2021-11-12 Created: 2021-11-12 Last updated: 2024-01-17Bibliographically approved
Wallin, E. & Servin, M. (2022). Data-driven model order reduction for granular media. Computational Particle Mechanics, 9, 15-28
Open this publication in new window or tab >>Data-driven model order reduction for granular media
2022 (English)In: Computational Particle Mechanics, ISSN 2196-4378, Vol. 9, p. 15-28Article in journal (Refereed) Published
Abstract [en]

We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-drivenapproach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distributionand system control signals. Rapid model inference of particle velocities replaces the intense process of computing contactforces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to outputthe interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the mediain domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particlemotion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regressionpredictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90%and 65%, respectively, and the speed-up range between 10 and 60. 

Place, publisher, year, edition, pages
Springer Nature, 2022
National Category
Other Physics Topics Applied Mechanics
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-169604 (URN)10.1007/s40571-020-00387-6 (DOI)000616428800001 ()2-s2.0-85101460553 (Scopus ID)
Funder
Vinnova, 2019-04832eSSENCE - An eScience Collaboration
Available from: 2020-04-08 Created: 2020-04-08 Last updated: 2023-03-24Bibliographically approved
Aoshima, K., Lindmark, D. & Servin, M. (2022). Examining the simulation-to-reality-gap of a wheel loader interacting with deformable terrain. In: : . Paper presented at 11th Asia-Pacific Regional Conference of the ISTVS September 26-28, 2022, Harbin, China. Harbin, China: International Society for Terrain-Vehicle Systems
Open this publication in new window or tab >>Examining the simulation-to-reality-gap of a wheel loader interacting with deformable terrain
2022 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Simulators are essential for developing autonomous control of off-road vehicles and heavy equipment. They allow automatic testing under safe and controllable conditions, and the generation of large amounts of synthetic and annotated training data necessary for deep learning to be applied [1]. Limiting factors are the computational speed and how accurately the simulator reflects the real system. When the deviation is too large, a controller transfers poorly from the simulated to the real environment. On the other hand, a finely resolved simulator easily becomes too computationally intense and slow for running the necessary number of simulations or keeping realtime pace with hardware in the loop.

We investigate how well a physics-based simulator can be made to match its physical counterpart, a full-scale wheel loader instrumented with motion and force sensors performing a bucket filling operation [2]. The simulated vehicle is represented as a rigid multibody system with nonsmooth contact and driveline dynamics. The terrain model combines descriptions of the frictional-cohesive soil as a continuous solid and particles, discretized in voxels and discrete elements [3]. Strong and stable force coupling with the equipment is mediated via rigid aggregate bodies capturing the bulk mechanics of the soil. The results include analysis of the agreement between a calibrated simulation model and the field tests, and of how the simulation performance and accuracy depend on spatial and temporal resolution. The system’s degrees of freedom range from hundreds to millions and the simulation speed up to ten times faster than realtime. Furthermore, it is investigated how sensitive a deep learning controller is to variations in the simulator environment parameters.

[1]  S. Backman, D. Lindmark, K. Bodin, M. Servin, J. Mörk, and H. Löfgren. Continuous control of an underground loader using deep reinforcement learning. Machines 9(10): 216 (2021).

[2]  K. Aoshima, M. Servin, E. Wadbro. Simulation-Based Optimization of High-Performance Wheel Loading. Proc. 38th Int. Symp. Automation and Robotics in Construction (ISARC), Dubai, UAE (2021).

[3]  M. Servin., T. Berglund., and S. Nystedt. A multiscale model of terrain dynamics for real-time earthmoving simulation. Advanced Modeling and Simulation in Engineering Sciences 8, 11 (2021). 

Place, publisher, year, edition, pages
Harbin, China: International Society for Terrain-Vehicle Systems, 2022
Keywords
Earthmoving, Simulation, Deep learning, Autonomous control, Deformable terrain, sim2real
National Category
Computer Vision and Robotics (Autonomous Systems) Applied Mechanics Computer Sciences
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-196337 (URN)
Conference
11th Asia-Pacific Regional Conference of the ISTVS September 26-28, 2022, Harbin, China
Funder
Swedish National Infrastructure for Computing (SNIC), SNIC 2022/5-251
Available from: 2022-06-12 Created: 2022-06-12 Last updated: 2022-06-23
Wallin, E., Wiberg, V., Vesterlund, F., Holmgren, J., Persson, H. & Servin, M. (2022). Learning multiobjective rough terrain traversability. Journal of terramechanics, 102, 17-26
Open this publication in new window or tab >>Learning multiobjective rough terrain traversability
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2022 (English)In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 102, p. 17-26Article in journal (Refereed) Published
Abstract [en]

We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal path planning over large areas.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Traversability, Rough terrain vehicle, Multibody simulation, Laser scan, Deep learning
National Category
Robotics
Research subject
Physics; Computerized Image Analysis; data science; Physics
Identifiers
urn:nbn:se:umu:diva-193680 (URN)10.1016/j.jterra.2022.04.002 (DOI)000805039700001 ()2-s2.0-85129503568 (Scopus ID)
Projects
Mistra Digital Forest
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, 2017/14 #6eSSENCE - An eScience CollaborationSwedish National Infrastructure for Computing (SNIC), 2021/5-234
Available from: 2022-04-11 Created: 2022-04-11 Last updated: 2023-05-08Bibliographically approved
Wiberg, V., Wallin, E., Wadbro, E., Rosander, M. & Servin, M. (2022). Simulation-to-reality transfer to control a forwarder with active suspensions through deep reinforcement learning. In: International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA: . Paper presented at International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, October 4-7, 2022.
Open this publication in new window or tab >>Simulation-to-reality transfer to control a forwarder with active suspensions through deep reinforcement learning
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2022 (English)In: International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, 2022Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Automating the loaded and unloaded driving of a forwarder has the potential to reduce operational costsup to 10% in cut-to-length logging, but remains a challening and unsolved task. The complex interaction between the vehicle and terrain requires the controller to percieve its surroundings and thestate of the vehicle to plan for traversal. Because the state space is high dimensional and the systemdynamics cannot be formulated in closed form or easily approximated, traditional control methods areinadequate. Under these conditions, where learning to act in the environement is easier than learning thesystem dynamics, model free reinforcement learning is a promising option. We use deep reinforcementlearning for control of a 16-tonne forwarder with actively articulated suspensions. To efficiently gathergeneralizable experience, the control policies were safetly trained in simulation while varying severaldomain parameters. Each policy is trained during what correponds to roughly one month of real time.In simulation, the controller shows the ability to traverse rough terrains reconstruced from high-densitylaser scans and handles slopes up to 27◦. To compare the simulated to real performance we transfer thecontrol policies to the physical vehicle. Our results provide insight on how to improve policy transfer toheavy and expensive forest machines.

National Category
Robotics Computer Vision and Robotics (Autonomous Systems) Other Physics Topics
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-199447 (URN)
Conference
International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, October 4-7, 2022
Available from: 2022-09-17 Created: 2022-09-17 Last updated: 2022-09-19
Lundbäck, M., Nordfjell, T., Wiberg, V., Wallin, E. & Servin, M. (2022). Traversability analysis using high-resolution laser-scans, simulation, and deep learning. In: : . Paper presented at International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, October 4-7, 2022.
Open this publication in new window or tab >>Traversability analysis using high-resolution laser-scans, simulation, and deep learning
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2022 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

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.

National Category
Computer Vision and Robotics (Autonomous Systems) Robotics Other Physics Topics
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-199448 (URN)
Conference
International Conference of Forest Engineering COFE-FORMEC-IUFRO, Corvallis, Oregon, USA, October 4-7, 2022
Funder
Mistra - The Swedish Foundation for Strategic Environmental Research, 2017/14 #6
Available from: 2022-09-17 Created: 2022-09-17 Last updated: 2023-07-03
Servin, M., Berglund, T. & Nystedt, S. (2021). A multiscale model of terrain dynamics for real-time earthmoving simulation. Advanced Modeling and Simulation in Engineering Sciences, 8(1), Article ID 11.
Open this publication in new window or tab >>A multiscale model of terrain dynamics for real-time earthmoving simulation
2021 (English)In: Advanced Modeling and Simulation in Engineering Sciences, E-ISSN 2213-7467, Vol. 8, no 1, article id 11Article in journal (Other academic) Published
Abstract [en]

A multiscale model for real-time simulation of terrain dynamics is explored. To represent the dynamics on different scales the model combines the description of soil as a continuous solid, as distinct particles and as rigid multibodies. The models are dynamically coupled to each other and to the earthmoving equipment. Agitated soil is represented by a hybrid of contacting particles and continuum solid, with the moving equipment and resting soil as geometric boundaries. Each zone of active soil is aggregated into distinct bodies, with the proper mass, momentum and frictional-cohesive properties, which constrain the equipment’s multibody dynamics. The particle model parameters are pre-calibrated to the bulk mechanical parameters for a wide range of different soils. The result is a computationally efficient model for earthmoving operations that resolve the motion of the soil, using a fast iterative solver, and provide realistic forces and dynamic for the equipment, using a direct solver for high numerical precision. Numerical simulations of excavation and bulldozing operations are performed to test the model and measure the computational performance. Reference data is produced using coupled discrete element and multibody dynamics simulations at relatively high resolution. The digging resistance and soil displacements with the real-time multiscale model agree with the reference model up to 10–25%, and run more than three orders of magnitude faster.

Place, publisher, year, edition, pages
Springer, 2021
Keywords
Deformable terrain, Discrete element method, Multibody dynamics, Multiscale, Real-time simulation, Soil mechanics
National Category
Applied Mechanics Computational Mathematics Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-176350 (URN)10.1186/s40323-021-00196-3 (DOI)2-s2.0-85105991856 (Scopus ID)
Funder
eSSENCE - An eScience Collaboration
Available from: 2020-11-01 Created: 2020-11-01 Last updated: 2023-03-24Bibliographically approved
Backman, S., Lindmark, D., Bodin, K., Servin, M., Mörk, J. & Löfgren, H. (2021). Continuous Control of an Underground Loader Using Deep Reinforcement Learning. Machines, 9(10), Article ID 216.
Open this publication in new window or tab >>Continuous Control of an Underground Loader Using Deep Reinforcement Learning
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2021 (English)In: Machines, E-ISSN 2075-1702, Vol. 9, no 10, article id 216Article in journal (Refereed) Published
Abstract [en]

The reinforcement learning control of an underground loader was investigated in a simulated environment by using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of a pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point while avoiding collisions, getting stuck, or losing ground traction. This relies on motion and force sensors, as well as on a camera and lidar. Using a soft actor–critic algorithm, the agents learn policies for efficient bucket filling over many subsequent loading cycles, with a clear ability to adapt to the changing environment. The best results—on average, 75% of the max capacity—were obtained when including a penalty for energy usage in the reward.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
autonomous excavation, bucket filling, deep reinforcement learning, mining robotics, simulation, wheel loader
National Category
Robotics Computer Sciences Applied Mechanics
Research subject
Physics; Computer Science
Identifiers
urn:nbn:se:umu:diva-187947 (URN)10.3390/machines9100216 (DOI)000717124100001 ()2-s2.0-85116361998 (Scopus ID)
Funder
Vinnova, 2019-04832
Available from: 2021-09-27 Created: 2021-09-27 Last updated: 2023-09-05Bibliographically approved
Projects
Computer vision in granular processes by real-time physics [2016-03442_Vinnova]; Umeå University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0787-4988

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