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Obstacle identification through fast vector analysis
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. ÅF-Engineering AB.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0002-9862-828X
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0002-2342-1647
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
Number of Authors: 42016 (English)In: ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, New York: American Society of Mechanical Engineers , 2016, Vol. 3, article id DETC2016-59881Conference paper, Published paper (Refereed)
Abstract [en]

During road travel, obstacles can impede productivity or durability for many different vehicles and render discomfort or injuries for the people within. Using remote sensing techniques, information from the surroundings can be acquired and analysed to identify obstacles ahead. The subsequent analysis can create a decision support for how the vehicle or driver should act upon encountered obstacles, through either autonomous control, guidance to the driver or a combination of both. In this paper, an experimental setup was created to mimic an obstacle in the shape of a speed bump on a flat road. An RGB-D camera was used to acquire information while travelling towards the speed bump. Afterwards, the acquired information was analysed by an estimation of the normal vector for each point in a 2D depth map. The resulting data from the experiments had sufficient resolution, speed and quality to retrieve proper identify obstacles or targets indoors with an accuracy of 2%. Obstacles were measured and identified in less than 20~ms where processing time mainly comprised data transfer from the USB-bus. The obstacle identification can be used to e.g. actively control the vehicle suspension, send feedback to the driver about obstacles ahead or optimise speed and direction for autonomous vehicles.

Place, publisher, year, edition, pages
New York: American Society of Mechanical Engineers , 2016. Vol. 3, article id DETC2016-59881
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design; Smart machines and materials (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-35094DOI: 10.1115/DETC2016-59881ISI: 000393364300002Scopus ID: 2-s2.0-85007352519Local ID: 97be841e-7edc-40c1-90ac-1e74fabab71dISBN: 9780791850138 (print)OAI: oai:DiVA.org:ltu-35094DiVA, id: diva2:1008346
Conference
18th International Conference on Advanced Vehicle Technologies; 13th International Conference on Design Education; 9th Frontiers in Biomedical Devices Charlotte, North Carolina, USA, August 21–24, 2016
Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-14Bibliographically approved
In thesis
1. A Methodology for Automation of Mechanized Forest Regeneration
Open this publication in new window or tab >>A Methodology for Automation of Mechanized Forest Regeneration
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
En Metodik för Automation av Mekaniserad Skogsföryngring
Abstract [en]

High quality forest regeneration is typically performed by a combination of site preparation through mounding and deep planting. These operations are performed either by mounders with subsequent manual planting, or by mechanized tree planting devices that creates mounds and, simultaneously, plant seedlings. These forest regeneration strategies are sustainably beneficial and have high development potential particularly regarding efficiency. Recent development in digitalization, sensing, IoT etc. has enabled new forest regeneration strategies. Hence, the objective of this thesis is therefore to develop a methodology for how automation can be used to improve sustainability in forestry and in forest regeneration operations in particular. The research has followed the Design Research Methodology in which success criteria was identified concurrently with analysis of the as-is situation. Then different sub-solutions forming the automation methodology was prescribed and validated in subsequent descriptive studies. To enable automation, it was found that data needs to be retrieved from surroundings while performing mounding or mechanized planting, whereby analyses of this data need to be used to make improved decisions of the machines’ working procedures. First, properties and characteristics of the surroundings on a clearcut were defined. Then, different ways to retrieve data from the surroundings were tested and evaluated. In addition, several ways of analyzing such data for improved decision making were found and validated. The information gained from data collection of surroundings and subsequent analyses further has potential to be improve activities beyond forest regeneration e.g. smart forwarding, customer adapted assortments, history tracking etc. To use as support for how decision making can be improved from such information, an experimental terrain vehicle platform for researchers and machine developers was developed to enable tests and validation of new solutions with special focus on autonomy and robotics, which requires a variety of data collected from both exteroceptive and proprioceptive sensors. The resulting methodology shows how forest regeneration can be automated in the short-term. Developed methods for data collection and analysis can enhance obstacle avoidance for mounding significantly, and thus contribute to increasing the share of continuously intermittent mounding conducted in Fennoscandia.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Mechanical Engineering Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-68031 (URN)978-91-7790-078-8 (ISBN)978-91-7790-079-5 (ISBN)
Public defence
2018-06-05, E632, Luleå tekniska universitet, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-03-26 Created: 2018-03-22 Last updated: 2018-05-24Bibliographically approved

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Vingbäck, JohanLideskog, HåkanKarlberg, MagnusJeppsson, Peter

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