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Realization of Autonomous Detection, Positioning and Angle Estimation of Harvested Logs
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. (Machine Design)ORCID iD: 0000-0001-9965-6955
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. (Machine Design)ORCID iD: 0000-0002-9862-828x
2023 (English)In: Croatian Journal of Forest Engineering, ISSN 1845-5719, E-ISSN 1848-9672, Vol. 44, no 2, p. 369-383Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
University of Zagreb , 2023. Vol. 44, no 2, p. 369-383
Keywords [en]
log detection, autonomous forwarding, log grasping
National Category
Computer Vision and Robotics (Autonomous Systems) Forest Science
Research subject
Machine Design
Identifiers
URN: urn:nbn:se:ltu:diva-89939DOI: 10.5552/crojfe.2023.2056ISI: 001088744700013Scopus ID: 2-s2.0-85165462778OAI: oai:DiVA.org:ltu-89939DiVA, id: diva2:1648046
Funder
VinnovaSwedish Energy AgencyInterreg
Note

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

Available from: 2022-03-29 Created: 2022-03-29 Last updated: 2024-03-07Bibliographically approved
In thesis
1. Object Identification for Autonomous Forest Operations
Open this publication in new window or tab >>Object Identification for Autonomous Forest Operations
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The need to further unlock productivity of forestry operations urges the increase of forestry automation. Many essential operations in forest production, such as harvesting, forwarding, planting, etc., have the potential to be automated and obtain benefits such as improved production efficiency, reduced operating costs, and an improved working environment. In view of the fact that forestry operations are performed in forest environments, the automation of forestry operations is thus complex and extremely challenging. To build the ability of forest machine automation, it is necessary to construct an environmental cognitive ability of the forest machine as basis. Through a combination of exteroceptive sensors and algorithms, forest machine vision can be realized. Using new and off-the-shelf solutions for detecting, locating, classifying and analyzing the status of objects of concern surrounding the machine during forestry operations in combination with smart judgement and control, forest operations can be automated. This thesis focuses on the introduction of vision systems on an unmanned forest platform, aiming to create the foundation for autonomous decision-making and execution in forestry operations. Initially, the vision system is designed to work on an unmanned forest machine platform, to create necessary conditions to either assist operators or to realize automatic operation as a further step.

In this thesis, vision systems based on stereo camera sensing are designed and deployed on an unmanned forest machine platform and the functions of detection, localization and pose estimation of objects that surround the machine are developed and evaluated. These mainly include a positioning function for forest terrain obstacles such as stones and stumps based on stereo camera data and deep learning, and a localization and pose estimation function for ground logs based on stereo camera and deep learning with added functionality of color difference comparison. By testing these systems’ performance in realistic scenarios, this thesis describe the feasibility of improving the automation level of forest machine operation by building a vision system. In addition, the thesis also demonstrate that the accuracy of stump detection can be improved without significantly increasing the processing load by introducing depth information into training and execution.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Machine Design
Identifiers
urn:nbn:se:ltu:diva-90263 (URN)978-91-8048-077-2 (ISBN)978-91-8048-078-9 (ISBN)
Presentation
2022-06-16, E632, Luleå tekniska universitet, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2022-04-21 Created: 2022-04-20 Last updated: 2023-09-05Bibliographically approved
2. Vision based perception systems for unmanned forestry machines
Open this publication in new window or tab >>Vision based perception systems for unmanned forestry machines
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2023
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Vehicle Engineering
Research subject
Machine Design
Identifiers
urn:nbn:se:ltu:diva-101829 (URN)978-91-8048-420-6 (ISBN)978-91-8048-421-3 (ISBN)
Public defence
2024-01-11, E632, Luleå tekniska universitet, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-10-30 Created: 2023-10-27 Last updated: 2023-12-08Bibliographically approved

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Li, SongyuLideskog, Håkan

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