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Vision based perception systems for unmanned forestry machines
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Produkt- och produktionsutveckling.ORCID-id: 0000-0001-9965-6955
2023 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

After years of continuous development in the past decades, the field of forest production has achieved a notable degree of mechanization. Nonetheless, the unceasing demand for increased productivity in forestry operations continues to drive the rapid expansion of forestry automation. Critical aspects of forest production, including activities such as harvesting, forwarding, seedling planting, and soil preparation, hold significant potential for improved automation. These advancements promise numerous advantages, such as heightened production efficiency, reduced environmental impact, reduced operational costs, and the creation of a more favourable work environment. Beyond fostering industrial progress, such innovations could also make a positive contribution to the sustainable development of forestry.

Forestry operations take place in intricate natural forest environments, rendering the automation of these processes complex and challenging. To realize full automation of forest machines, it is imperative to equip them with environmental awareness and cognitive capabilities. This can be achieved by integrating advanced imaging sensors with sophisticated algorithms, thereby enabling forest machinery to possess a vision system. By utilizing both novel and existing solutions for object detection, positioning, categorization, and status analysis within the machine's surroundings, combined with intelligent decision-making and control mechanisms, forest machinery can attain a higher degree of operational automation.

This thesis primarily focuses on the development, deployment, and validation of vision systems on an unmanned forestry machine platform, with a special emphasis on the prototype development of essential features that are still manually executed in contemporary forestry operations. These features include forest terrain obstacle detection, roundwood pose estimation, automated selection of seedling planting locations, and autonomous obstacle avoidance for mounders.

In this thesis, vision systems rooted in color and stereo camera sensing are designed and deployed on an unmanned forest machinery platform. These systems encompass the following key functionalities:

•             A precise positioning system for detecting forest terrain obstacles, such as stones and stumps, using stereo camera data in conjunction with deep learning techniques.

•             Localization and pose estimation capabilities for ground logs, leveraging stereo cameras and deep learning.

•             An analysis of the planning area for obstacle detection, along with the extraction of feasible planting zones for establishing seedling planting locations.

•             The development of an automated obstacle avoidance system for forestry mounders, powered by visual solutions.

Through rigorous testing in real-world scenarios spanning logging, loading, planting, and site preparation, this paper demonstrates the feasibility and practicality of enhancing the level of automation in forest machinery operations through the integration of vision systems. It culminates in the creation of tangible and efficient functional prototypes, leading towards a new era of automation in the field of forestry.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2023.
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
HSV kategori
Forskningsprogram
Maskinkonstruktion
Identifikatorer
URN: urn:nbn:se:ltu:diva-101829ISBN: 978-91-8048-420-6 (tryckt)ISBN: 978-91-8048-421-3 (digital)OAI: oai:DiVA.org:ltu-101829DiVA, id: diva2:1807829
Disputas
2024-01-11, E632, Luleå tekniska universitet, Luleå, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2023-10-30 Laget: 2023-10-27 Sist oppdatert: 2023-12-08bibliografisk kontrollert
Delarbeid
1. Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land
Åpne denne publikasjonen i ny fane eller vindu >>Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land
2021 (engelsk)Inngår i: Forests, E-ISSN 1999-4907, Vol. 12, nr 9, artikkel-id 1142Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Research highlights: An automatic localization system for ground obstacles on harvested forest land based on existing mature hardware and software architecture has been successfully implemented. In the tested area, 98% of objects were successfully detected and could on average be positioned within 0.33 m from their true position in the full range 1–10 m from the camera sensor. Background and objectives: Forestry operations in forest environments are full of challenges; detection and localization of objects in complex forest terrains often require a lot of patience and energy from operators. Successful automatic real-time detection and localization of terrain objects not only can reduce the difficulty for operators but are essential for the automation of harvesting and logging tasks. We intend to implement a system prototype that can automatically locate ground obstacles on harvested forest land based on accessible hardware and common software infrastructure. Materials and Methods: An automatic object detection and localization system based on stereo camera sensing is described and evaluated in this paper. This demonstrated system detects and locates objects of interest automatically utilizing the YOLO (You Only Look Once) object detection algorithm and derivation of object positions in 3D space. System performance is evaluated by comparing the automatic detection results of the tests to manual labeling and positioning results. Results: Results show high reliability of the system for automatic detection and location of stumps and large stones and shows good potential for practical application. Overall, object detection on test tracks was 98% successful, and positional location errors were on average 0.33 m in the full range from 1–10 m from the camera sensor. Conclusions: The results indicate that object detection and localization can be used for better operator assessment of surroundings, as well as input to control machines and equipment for object avoidance or targeting.

sted, utgiver, år, opplag, sider
MDPI, 2021
Emneord
automation, object identification, obstacle detection, terrain obstacle
HSV kategori
Forskningsprogram
Maskinkonstruktion
Identifikatorer
urn:nbn:se:ltu:diva-86809 (URN)10.3390/f12091142 (DOI)000701035100001 ()2-s2.0-85114723919 (Scopus ID)
Forskningsfinansiär
Swedish Energy Agency, 48003-1Vinnova, 2020-04202EU, Horizon 2020, H2020-EU.3.5.1.European Regional Development Fund (ERDF), NYPS 20202905
Merknad

Validerad;2021;Nivå 2;2021-09-01 (johcin)

Tilgjengelig fra: 2021-08-25 Laget: 2021-08-25 Sist oppdatert: 2024-07-04bibliografisk kontrollert
2. Realization of Autonomous Detection, Positioning and Angle Estimation of Harvested Logs
Åpne denne publikasjonen i ny fane eller vindu >>Realization of Autonomous Detection, Positioning and Angle Estimation of Harvested Logs
2023 (engelsk)Inngår i: Croatian Journal of Forest Engineering, ISSN 1845-5719, E-ISSN 1848-9672, Vol. 44, nr 2, s. 369-383Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

To further develop forest production, higher automation of forest operations is required. Such endeavour promotes research on unmanned forest machines. Designing unmanned forest machines that exercise forwarding requires an understanding of positioning and angle estimations of logs after cutting and delimbing have been conducted, as support for subsequent crane loading work. This study aims to improve the automation of the forwarding operation and presents a system to realize real-time automatic detection, positioning, and angle estimation of harvested logs implemented on an existing unmanned forest machine experimental platform from the AORO (Arctic Off-Road Robotics) Lab. This system uses ROS as the underlying software architecture and a Zed2 camera and NVIDIA JETSON AGX XAVIER as the imaging sensor and computing platform, respectively, utilizing the YOLOv3 algorithm for real-time object detection. Moreover, the study combines the processing of depth data and depth to spatial transform to realize the calculation of the relative location of the target log related to the camera. On this basis, the angle estimation of the target log is further realized by image processing and color analysis. Finally, the absolute position and log angles are determined by the spatial coordinate transformation of the relative position data. This system was tested and validated using a pre-trained log detector for birch with a mean average precision (mAP) of 80.51%. Log positioning mean error did not exceed 0.27 m and the angle estimation mean error was less than 3 degrees during the tests. This log pose estimation method could encompass one important part of automated forwarding operations.

sted, utgiver, år, opplag, sider
University of Zagreb, 2023
Emneord
log detection, autonomous forwarding, log grasping
HSV kategori
Forskningsprogram
Maskinkonstruktion
Identifikatorer
urn:nbn:se:ltu:diva-89939 (URN)10.5552/crojfe.2023.2056 (DOI)001088744700013 ()2-s2.0-85165462778 (Scopus ID)
Forskningsfinansiär
VinnovaSwedish Energy AgencyInterreg
Merknad

Validerad;2023;Nivå 2;2023-06-27 (hanlid)

Tilgjengelig fra: 2022-03-29 Laget: 2022-03-29 Sist oppdatert: 2024-03-07bibliografisk kontrollert
3. Vision-based planting position selection system for an unmanned reforestation machine 
Åpne denne publikasjonen i ny fane eller vindu >>Vision-based planting position selection system for an unmanned reforestation machine 
2024 (engelsk)Inngår i: Forestry (London), ISSN 0015-752X, E-ISSN 1464-3626, artikkel-id cpae032Artikkel i tidsskrift (Fagfellevurdert) Epub ahead of print
Abstract [en]

Research on automated seedling planting systems in forestry is a crucial aspect of forestry automation. This paper introduces the development of a vision-based automated seedling planting position selection system, integrated with hardware and software components on an unmanned forest machine platform. Developed around object detection as the core, this research presents a comprehensive system consisting of two main functionalities: (i) A vision system that performs obstacle detection and localization, providing estimated obstacle types, sizes, and positions to the plant planner function. (ii) A plant planner function utilizes this information to plan the plantable areas and selects suitable planting locations. The integrated system has been tested in the field and we found it to effectively determine suitable planting locations on the ground of a clear-cut. The implementation of this system lays the foundation for subsequent automated planting operations. Furthermore, the automation of forest seedling planting reduces the need for manual labor and enhances planting precision, contributing to improved forest health and ecological balance. Looking ahead, this research offers insights into the future development of unmanned forestry operations, making strides in automating forest management, achieving cost-effectiveness, and facilitating ecological restoration. 

sted, utgiver, år, opplag, sider
Oxford University Press, 2024
Emneord
forestry planting, planting position selection, unmanned forest machine
HSV kategori
Forskningsprogram
Maskinkonstruktion
Identifikatorer
urn:nbn:se:ltu:diva-101678 (URN)10.1093/forestry/cpae032 (DOI)
Forskningsfinansiär
Vinnova, 2020-04202
Merknad

Full text: CC BY License;

This article has previously appeared as a manuscript in a thesis.

Tilgjengelig fra: 2023-10-16 Laget: 2023-10-16 Sist oppdatert: 2024-06-28
4. Obstacle detection vision system enabling autonomous mounding on clearcuts
Åpne denne publikasjonen i ny fane eller vindu >>Obstacle detection vision system enabling autonomous mounding on clearcuts
2023 (engelsk)Inngår i: Proceedings of the 16th European-African Regional Conference of the ISTVS, International Society for Terrain-Vehicle Systems , 2023, artikkel-id 2765Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
International Society for Terrain-Vehicle Systems, 2023
HSV kategori
Forskningsprogram
Maskinkonstruktion
Identifikatorer
urn:nbn:se:ltu:diva-101677 (URN)10.56884/VQZG2856 (DOI)
Konferanse
16th European-African Regional Conference of the ISTVS, Lublin, Poland, October 11-13, 2023
Forskningsfinansiär
Vinnova, 2020-04202Interreg, 20357984
Merknad

Funder: EU (869580);

ISBN for host publication: 978-1-942112-55-6

Tilgjengelig fra: 2023-10-16 Laget: 2023-10-16 Sist oppdatert: 2024-07-23bibliografisk kontrollert
5. Forest Terrain Object Detection Based on RGB and Depth Information
Åpne denne publikasjonen i ny fane eller vindu >>Forest Terrain Object Detection Based on RGB and Depth Information
2021 (engelsk)Inngår i: Proceedings of the 20th International Conference and 9th Americas Conference of the International Society for Terrain-Vehicle Systems / [ed] Martelli M.; Kovecses J.; Shenvi M.; Dixon J., International Society for Terrain-Vehicle Systems , 2021Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

At present, there is extensive ongoing research and development on autonomous road vehicles, but much less effort is put on autonomous driving in off-road environments, especially with regard to autonomous machines driving in forest terrain. At the same time, forest machine work is harsh for the operator, and the operator’s maximum performance is a major bottleneck for machine operations. One way to enable a safe work environment is to remove the driver from the machine. Thus, forestry machines must become autonomous in order to improve personnel safety and work efficiency. The basis of enabling automation of forest machinery is highly precise and accurate acquisition of working environment information, especially reliable virtual representation of forest terrain and obstacles within. In this paper, the objective is to improve the accuracy of object detection in forest terrain based on an AI algorithm by adding the depth variation texture to corresponding area of  RGB images. The results show that the algorithm significantly improves the accuracy of object detection while the probability of false detection is reduced. Meanwhile, computational efficiency compared to previous image processing is not reduced to such extent that it prevents real-time computation.

sted, utgiver, år, opplag, sider
International Society for Terrain-Vehicle Systems, 2021
Emneord
Forest Machine, Stump Detection, Machine Learning, Stereo Camera
HSV kategori
Forskningsprogram
Maskinkonstruktion
Identifikatorer
urn:nbn:se:ltu:diva-83698 (URN)2-s2.0-85124530071 (Scopus ID)
Konferanse
20th International Conference and 9th Americas Conference of the International Society for Terrain-Vehicle Systems (ISTVS 2021), [Online], September 27-29, 2021
Forskningsfinansiär
Interreg NordVinnovaEU, Horizon 2020, 869580
Tilgjengelig fra: 2021-04-15 Laget: 2021-04-15 Sist oppdatert: 2023-10-27bibliografisk kontrollert

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