hh.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Baerveldt, Albert-Jan
Alternative names
Publications (9 of 9) Show all publications
Brorsson, S., Nilsdotter, A., Sollerman, C., Baerveldt, A.-J. & Hilliges, M. (2008). A new force measurement device for evaluating finger extension function in the healthy and rheumatoid arthritis hand. Technology and Health Care, 16(4), 283-292
Open this publication in new window or tab >>A new force measurement device for evaluating finger extension function in the healthy and rheumatoid arthritis hand
Show others...
2008 (English)In: Technology and Health Care, ISSN 0928-7329, E-ISSN 1878-7401, Vol. 16, no 4, p. 283-292Article in journal (Refereed) Published
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

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2008
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:hh:diva-5970 (URN)18776605 (PubMedID)2-s2.0-52449125684 (Scopus ID)
Available from: 2010-09-23 Created: 2010-09-23 Last updated: 2018-03-23Bibliographically approved
Antonelo, E. A., Baerveldt, A.-J., Rögnvaldsson, T. & Figueiredo, M. (2006). Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors. In: International Joint Conference on Neural Networks, 2006. IJCNN '06. Paper presented at International Joint Conference on Neural Networks, 2006. IJCNN '06, Vancouver (pp. 498-505). Piscataway, N.J.: IEEE Press
Open this publication in new window or tab >>Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors
2006 (English)In: International Joint Conference on Neural Networks, 2006. IJCNN '06, Piscataway, N.J.: IEEE Press, 2006, p. 498-505Conference paper, Published 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.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2006
Series
IEEE International Joint Conference on Neural Networks (IJCNN), ISSN 1098-7576
Keywords
mobile robots, neurocontrollers, path planning
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-2112 (URN)10.1109/IJCNN.2006.246723 (DOI)000245125900073 ()2-s2.0-40649114292 (Scopus ID)2082/2507 (Local ID)0-7803-9490-9 (ISBN)2082/2507 (Archive number)2082/2507 (OAI)
Conference
International Joint Conference on Neural Networks, 2006. IJCNN '06, Vancouver
Note

©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2008-11-07 Created: 2008-11-07 Last updated: 2018-03-23Bibliographically approved
Åstrand, B. & Baerveldt, A.-J. (2005). A vision based row-following system for agricultural field machinery. Mechatronics (Oxford), 15(2), 251-269
Open this publication in new window or tab >>A vision based row-following system for agricultural field machinery
2005 (English)In: Mechatronics (Oxford), ISSN 0957-4158, E-ISSN 1873-4006, Vol. 15, no 2, p. 251-269Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2005
Keywords
Agricultural robots, Row-following, Vision-guidance, Crop-row location, Hough transforms
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-239 (URN)10.1016/j.mechatronics.2004.05.005 (DOI)000226871200007 ()2-s2.0-12244301226 (Scopus ID)2082/534 (Local ID)2082/534 (Archive number)2082/534 (OAI)
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2018-03-23Bibliographically approved
Olandersson, S., Lundqvist, H., Bengtsson, M., Lundahl, M., Baerveldt, A.-J. & Hilliges, M. (2005). Finger-force measurement-device for hand rehabilitation. In: 2005 IEEE 9th International Conference on Rehabilitation Robotics: Chicago, IL, 28 June - 1 July 2005. Paper presented at IEEE 9th International Conference on Rehabilitation Robotics, June 28-July 01, 2005, Chicago, IL (pp. 135-138). Piscataway, N.J.: IEEE Press
Open this publication in new window or tab >>Finger-force measurement-device for hand rehabilitation
Show others...
2005 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2005
Series
International Conference on Rehabilitation Robotics ICORR, ISSN 1945-7898
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-18726 (URN)10.1109/ICORR.2005.1501069 (DOI)000233375400032 ()2-s2.0-33745770861 (Scopus ID)0-7803-9003-2 (ISBN)
Conference
IEEE 9th International Conference on Rehabilitation Robotics, June 28-July 01, 2005, Chicago, IL
Available from: 2012-10-16 Created: 2012-06-25 Last updated: 2018-03-22Bibliographically approved
Antonelo, E. A., Figueiredo, M. F., Baerveldt, A. J. & Calvo, R. A. (2005). Intelligent autonomous navigation for mobile robots: Spatial concept acquisition and object discrimination. In: 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Proceedings. Paper presented at 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2005, Espoo, Finland, 27-30 June 2005 (pp. 553-557). New York: IEEE Press
Open this publication in new window or tab >>Intelligent autonomous navigation for mobile robots: Spatial concept acquisition and object discrimination
2005 (English)In: 2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Proceedings, New York: IEEE Press, 2005, p. 553-557Conference paper, Published 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.

Place, publisher, year, edition, pages
New York: IEEE Press, 2005
Keywords
intelligent autonomous navigation, neural networks, reinforcement learning, mobile robots, biologically inspired models
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-18729 (URN)10.1109/CIRA.2005.1554335 (DOI)000231588200092 ()2-s2.0-28444452560 (Scopus ID)0-7803-9355-4 (ISBN)
Conference
2005 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2005, Espoo, Finland, 27-30 June 2005
Note

Category number 05EX1153; Code 66105

Available from: 2012-10-16 Created: 2012-06-25 Last updated: 2018-03-22Bibliographically approved
Hedenberg, K. & Baerveldt, A.-J. (2004). Stereo vision-based collision avoidance. In: The 9th Mechatronics Forum International Conference: Conference Proceedings. Paper presented at Mechatronics 2004, 9th Mechatronics Forum International Conference, Ankara, Turkey, Aug. 30 – Sep. 1, 2004 (pp. 259-270). Ankara: Atılım University
Open this publication in new window or tab >>Stereo vision-based collision avoidance
2004 (English)In: The 9th Mechatronics Forum International Conference: Conference Proceedings, Ankara: Atılım University , 2004, p. 259-270Conference paper, Published 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.

Place, publisher, year, edition, pages
Ankara: Atılım University, 2004
Series
Atılım University Publications ; 20
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-25344 (URN)9756707135 (ISBN)9789756707135 (ISBN)
Conference
Mechatronics 2004, 9th Mechatronics Forum International Conference, Ankara, Turkey, Aug. 30 – Sep. 1, 2004
Available from: 2014-05-14 Created: 2014-05-14 Last updated: 2018-03-22Bibliographically approved
Bengtsson, O. & Baerveldt, A.-J. (2001). Localization in changing environments - Estimation of a covariance matrix for the IDC algorithm. In: 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. Paper presented at International Conference on Intelligent Robots and Systems, expanding the societal role of robotics in the next millennium , October 29-November 3, 2001, Outrigger Wailea Resort, Maui, Hawaii, USA (pp. 1931-1937). Piscataway, N.J.: IEEE
Open this publication in new window or tab >>Localization in changing environments - Estimation of a covariance matrix for the IDC algorithm
2001 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE, 2001
National Category
Signal Processing Probability Theory and Statistics Control Engineering
Identifiers
urn:nbn:se:hh:diva-35791 (URN)10.1109/IROS.2001.976356 (DOI)000176593900306 ()2-s2.0-0035560165 (Scopus ID)0-7803-6612-3 (ISBN)
Conference
International Conference on Intelligent Robots and Systems, expanding the societal role of robotics in the next millennium , October 29-November 3, 2001, Outrigger Wailea Resort, Maui, Hawaii, USA
Note

Funding: Volvo Research Foundation, Volvo Educational Foundation and Dr Pehr G Gyllenhammars Research Foundation.

Available from: 2018-03-28 Created: 2018-03-28 Last updated: 2018-07-19Bibliographically approved
Bengtsson, O. & Baerveldt, A.-J. (1999). Localization in changing environments by matching laser range scans. In: 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99).: Proceedings. Paper presented at Third European Workshop on Advanced Mobile Robots 1999 (Eurobot'99), Zurich, Switzerland, September 6-8, 1999 (pp. 169-176). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Localization in changing environments by matching laser range scans
1999 (English)In: 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99).: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 1999, p. 169-176Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 1999
Keywords
Changing environment, Laser range scan, Range scans, Real environments, Scan matching, Iterative methods
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-40873 (URN)10.1109/EURBOT.1999.827637 (DOI)2-s2.0-84991021808 (Scopus ID)0-7803-5672-1 (ISBN)
Conference
Third European Workshop on Advanced Mobile Robots 1999 (Eurobot'99), Zurich, Switzerland, September 6-8, 1999
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-11
Baerveldt, A.-J. & Klang, R. (1997). A low-cost and low-weight attitude estimation system for an autonomous helicopter. In: Imre J Rudas (Ed.), IEEE International Conference on Intelligent Engineering Systems, Proceedings, INES: . Paper presented at 1997 IEEE International Conference on Intelligent Engineering Systems : INES'97, September 15-17, 1997, Budapest, Hungary (pp. 391-395). Piscataway, N.J.: IEEE Press
Open this publication in new window or tab >>A low-cost and low-weight attitude estimation system for an autonomous helicopter
1997 (English)In: IEEE International Conference on Intelligent Engineering Systems, Proceedings, INES / [ed] Imre J Rudas, Piscataway, N.J.: IEEE Press, 1997, p. 391-395Conference paper, Published 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.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 1997
Keywords
Automation & Control Systems, Computer Science, Artificial Intelligence, Engineering, Manufacturing, Engineering
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-18813 (URN)A1997BJ96E00062 ()2-s2.0-0031355959 (Scopus ID)0-7803-3627-5 (ISBN)
Conference
1997 IEEE International Conference on Intelligent Engineering Systems : INES'97, September 15-17, 1997, Budapest, Hungary
Available from: 2012-08-16 Created: 2012-06-25 Last updated: 2018-03-22Bibliographically approved
Organisations

Search in DiVA

Show all publications