hh.sePublications
Change search
Refine search result
1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Antonelo, Eric A.
    et al.
    Electronics and Information Systems (ELIS) department, Ghent university, Belgium.
    Baerveldt, Albert-Jan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Figueiredo, Mauricio
    State University of Maringá, Brazil.
    Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors2006In: International Joint Conference on Neural Networks, 2006. IJCNN '06, Piscataway, N.J.: IEEE Press, 2006, p. 498-505Conference paper (Refereed)
    Abstract [en]

    Classical reinforcement learning mechanisms and a modular neural network are unified for conceiving an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Distinct design apparatuses are considered for tackling these navigation difficulties, for instance: 1) neuron parameter for memorizing neuron activities (also functioning as a learning factor), 2) reinforcement learning mechanisms for adjusting neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures.

  • 2.
    Antonelo, Eric Aislan
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Figueiredo, Maurício Fernandes
    Department of Computer Science, State University of Maringá, 87020-900, Maringá - PR, Brazil.
    Baerveldt, Albert Jan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Calvo, Rodrigo A.
    Department of Computer Science and Statistics, University of São Paulo, São Carlos, Brazil.
    Intelligent autonomous navigation for mobile robots: Spatial concept acquisition and object discrimination2005In: 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Proceedings, New York: IEEE Press, 2005, p. 553-557Conference paper (Refereed)
    Abstract [en]

    An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.

  • 3.
    Baerveldt, Albert-Jan
    et al.
    Halmstad University, School of Business and Engineering (SET).
    Klang, Robert
    Halmstad University, School of Business and Engineering (SET).
    A low-cost and low-weight attitude estimation system for an autonomous helicopter1997In: IEEE International Conference on Intelligent Engineering Systems, Proceedings, INES / [ed] Imre J Rudas, Piscataway, N.J.: IEEE Press, 1997, p. 391-395Conference paper (Other academic)
    Abstract [en]

    In this paper a low-cost and low-weight attitude estimation system for an autonomous helicopter is presented. The system is based on an inclinometer and a rate gyro. The data coming from the sensors is fused through a complementary filter. In this way the slow dynamics of the inclinometer can be effectively compensated. Tests have shown that we obtained a very effective attitude estimation system.

  • 4.
    Bengtsson, Ola
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Baerveldt, Albert-Jan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Localization in changing environments - Estimation of a covariance matrix for the IDC algorithm2001In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180): Volume 4 of 4, Piscataway, N.J.: IEEE, 2001, p. 1931-1937Conference paper (Refereed)
    Abstract [en]

    Previously we have presented a new scan-matching algorithm, based on the IDC - Iterative Dual Correspondence- algorithm, which showed a good localization performance even in the case of severe changes in the environment. The Problem of the IDC-algorithm is that there is no good way to estimate the covariance matrix of the position estimate, which prohibits an effective fusion with other position estimates from other sensors, e.g by means of the Kalman filter. In this paper we present a new way to estimate the covariance matrix, by estimating the Hessian matrix of the error function that is minimized by the IDC scan-matching algorithm. Simulation results show that the estimated covariance matrix correspond well to the real one.

  • 5.
    Bengtsson, Ola
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Baerveldt, Albert-Jan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Localization in changing environments by matching laser range scans1999In: 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99).: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 1999, p. 169-176Conference paper (Refereed)
    Abstract [en]

    We present a novel scan matching algorithm, IDC-S, Iterative Dual Correspondence-Sector, that matches range scans. The algorithm is based on the known Iterative Dual Correspondence, IDC, algorithm which has shown good performance in real environments. The improvement is that IDC-S is able to deal with relatively large changes in the environment. It divides the scan in several sectors, detects and removes those sectors that are changed and matches the scans only using unchanged sectors. IDC-S and other variants of IDC are extensively simulated and evaluated. The simulations show that IDC-S is very robust and can locate in many different kind of environments. We also show that it is possible to effectively combine the existing IDC algorithms with IDC-S, thus obtaining an algorithm that performs very well both in rectilinear as well as nonrectilinear environments, even when changed as much as 65%. © 1999 IEEE.

  • 6.
    Brorsson, Sofia
    et al.
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Biomechanics and Biomedicine.
    Nilsdotter, Anna
    R & D Center, Spenshult Hospital of Rheumatic Diseases, Halmstad, Sweden.
    Sollerman, Christer
    Department of Hand Surgery, Sahlgrenska University Hospital, Göteborg, Sweden.
    Baerveldt, Albert-Jan
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Biomechanics and Biomedicine.
    Hilliges, Marita
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS), Biomechanics and Biomedicine.
    A new force measurement device for evaluating finger extension function in the healthy and rheumatoid arthritis hand2008In: Technology and Health Care, ISSN 0928-7329, E-ISSN 1878-7401, Vol. 16, no 4, p. 283-292Article in journal (Refereed)
    Abstract [en]

    Although often neglected, finger extension force is of great importance for developing grip strength. This paper describes the design and evaluation of a new finger extension force measurement device (EX-it) based on the biomechanics of the hand. Measurement accuracy and test-retest reliability were analysed. The device allows measurements on single fingers as well as all the fingers (excluding the thumb) of both healthy and deformed hands. The coefficient of variation in the device was 1.8% of the applied load, and the test-retest reliability showed a coefficient of variation no more than 7.1% for healthy subjects. This study also provides reference values for finger extension force in healthy subjects and patients with rheumatoid arthritis (RA). Significant differences were found in extension strength between healthy subject and RA patients (men, p < 0.05 and women, p < 0.001). EX-it provides objective and reliable data on the extension force capacity of normal and dysfunctional hands and can be used to evaluate the outcome of therapeutic interventions after hand trauma or disease

  • 7.
    Hedenberg, Klas
    et al.
    University of Skövde, School of Technology and Society, Skövde, Sweden.
    Baerveldt, Albert-Jan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Stereo vision-based collision avoidance2004In: The 9th Mechatronics Forum International Conference: Conference Proceedings, Ankara: Atılım University , 2004, p. 259-270Conference paper (Refereed)
    Abstract [en]

    This paper investigates whether a stereo vision system based on points of interest is robust enough to detect obstacles for applications like a mobile robot in an industrial environment and for the visually impaired. Points of interest are extracted with a known method, called KLT. Two algorithms to solve the correspondence problem (Sum of Squared Difference and Variance Normalized Correlation) are used and evaluated as well as a combination of the two. An improvement is made if the two algorithms are combined. The tests show that stereo vision based on points of interest only can be used robustly for obstacle detection if there is enough texture on the obstacle. Otherwise too few points of interest on the object are detected and a reliable estimation of the distance to the object cannot be made.

  • 8.
    Olandersson, Sofia
    et al.
    Halmstad University, School of Business and Engineering (SET), Biological and Environmental Systems (BLESS).
    Lundqvist, Helene
    Bengtsson, Martin
    Lundahl, Magnus
    Baerveldt, Albert-Jan
    Halmstad University, School of Business and Engineering (SET), Biological and Environmental Systems (BLESS).
    Hilliges, Marita
    Halmstad University, School of Business and Engineering (SET), Biological and Environmental Systems (BLESS).
    Finger-force measurement-device for hand rehabilitation2005In: 2005 IEEE 9th International Conference on Rehabilitation Robotics: Chicago, IL, 28 June - 1 July 2005, Piscataway, N.J.: IEEE Press, 2005, p. 135-138Conference paper (Refereed)
    Abstract [en]

    The purpose was to develop an extension finger-force measurement device, and investigate the intra-individual repeatability. The design of the measuring device allows single finger force and whole hand measurements, and the repeatability error on extension finger forces was measured, both on the whole hand, as well as on individual fingers. The tests showed that a repeatability error of less then 15 % can be achieved for single finger measurements and less then 21 % for whole hand measurements.

  • 9.
    Åstrand, Björn
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Baerveldt, Albert-Jan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    A vision based row-following system for agricultural field machinery2005In: Mechatronics (Oxford), ISSN 0957-4158, E-ISSN 1873-4006, Vol. 15, no 2, p. 251-269Article in journal (Refereed)
    Abstract [en]

    In the future, mobile robots will most probably navigate through the fields autonomously to perform different kind of agricultural operations. As most crops are cultivated in rows, an important step towards this long-term goal is the development of a row-recognition system, which will allow a robot to accurately follow a row of plants. In this paper we describe a new method for robust recognition of plant rows based on the Hough transform. Our method adapts to the size of plants, is able to fuse information coming from two rows or more and is very robust against the presence of many weeds. The accuracy of the position estimation relative to the row proved to be good with a standard deviation between 0.6 and 1.2 cm depending on the plant size. The system has been tested on both an inter-row cultivator and a mobile robot. Extensive field tests have showed that the system is sufficiently accurate and fast to control the cultivator and the mobile robot in a closed-loop fashion with a standard deviation of the position of 2.7 and 2.3 cm, respectively. The vision system is also able to detect exceptional situations by itself, for example the occurrence of the end of a row.

1 - 9 of 9
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf