Skip to main content Accessibility help
×
Home
Hostname: page-component-888d5979f-ts5rl Total loading time: 0.252 Render date: 2021-10-26T07:50:14.554Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

Article contents

Trajectory estimation of a moving object using Kalman filter and Kohonen networks

Published online by Cambridge University Press:  01 September 2007

JaeHwei Park
Affiliation:
Department of Electronics Engineering, Pusan National University, San 30 Jangjeon-dong Kumjeong-ku, Busan 609-735, Korea.
JaeMu Yun
Affiliation:
Department of Electronics Engineering, Pusan National University, San 30 Jangjeon-dong Kumjeong-ku, Busan 609-735, Korea.
JangMyung Lee*
Affiliation:
Department of Electronics Engineering, Pusan National University, San 30 Jangjeon-dong Kumjeong-ku, Busan 609-735, Korea.
*
*Corresponding author. E-mail: jmlee@pusan.ac.kr

Summary

A novel approach to estimate the real-time moving trajectory of an object is proposed in this paper. The object's position is obtained from the image data of a charge coupled device (CCD) camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized cooperatively. Since the Kalman filter needs to approximate a nonlinear system into a linear model in order to estimate the states, there still exist errors as well as uncertainties. To resolve this problem, in this approach, the Kohonen networks, which have a high adaptability to the memory of the input–output relationship, are utilized for the nonlinear region. In addition to this, the Kohonen network, as a sort of neural network, can effectively adapt to the dynamic variations and become robust against noises. This approach is derived from the observation that the Kohonen network is a type of a self-organized map and is spatially oriented, which makes it suitable for determining the trajectories of moving objects. The superiority of the proposed algorithm compared with the extended Kalman filter is demonstrated through real experiments.

Type
Article
Information
Robotica , Volume 25 , Issue 5 , September 2007 , pp. 567 - 574
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Kim, W. C., Hwang, C. H., Choi, S. H. and Lee, J. M., “Efficient Tracking of a Moving Object Using Optimal Representative Blocks,” Proceedings of the International Conference on Control, Automation and Systems 2002 pp. 264–269.Google Scholar
2.Park, J. W., Park, J. H., Hur, H. R., Lee, J. M., Tagawa, K. and Haneda, H., “Capturing a Moving Object Using an Active Camera Mounted on a Mobile Robot,” Proceedings of the 5th International Symposium on Artificial Life and Robotics 2000 pp. 609–612.Google Scholar
3.Lin, C. S., Chang, P. R. and Luh, J. Y. S., “Formulation and optimization of cubic polynomial trajectories for industrial robot,” IEEE Trans. Autom. Control 23, 10661074 1983.CrossRefGoogle Scholar
4.Allen, P. K., Tmcenko, A., Yoshimi, B. and Michelman, P., “Trajectory Fltering and Pediction for Atomated Tacking and Grasping of a moving object,” Proceedings of the IEEE International Conference on Robotics and Automation 1992 pp. 1850–1856.Google Scholar
5.Ma, Y., Kosecka, J. and Sastry, S. S., “Vision guided navigation for a nonholonomic mobile robot,” IEEE Trans. Robot. Autom. 15 (3), 521536 1999.CrossRefGoogle Scholar
6.Kalman, R. E., “A new approach to linear filtering and prediction problems,” ASME Trans. J. Basic Eng. Series 82D, 3545 1960.CrossRefGoogle Scholar
7.Sorenson, H. W., “Kalman filtering techniques,” Adv. Control Syst. Theory Appl. 3, 219292 1966.Google Scholar
8.Ramachandra, K. V., “A Kalman tracking filter for estimating position, velocity and acceleration from noisy measurements of a 3-D radar,” Electro. Technol. 33, 6676 1989.Google Scholar
9.Chen, Y.-Y. and Young, K.-Y., “An Intelligent Radar Predictor for High-Speed Moving-Target Tracking,” Proceedings of the IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering 3 2002 pp. 16381641.Google Scholar
10.Roberts, J. M., Mills, D. J., Charnley, D. and Harris, C. J., “Improved Kalman Filter Initialization Using Neuro-Fuzzy Estimation,” Proceedings of the International Conference on Artificial Neural Networks 1995 pp. 329–334.Google Scholar
11.DeCruyenaere, J. P. and Hafez, H. M., “A Comparison Between Kalman Filters and Recurrent Neural Networks,” Proceedings of the International Conference on Artificial Neural Networks 4 1992 pp. 247251.Google Scholar
12.Sorensen, O., “Neural Networks for Non-Linear Control,” Proceedings of the 3rd IEEE Conference on Control Applications 1994 pp. 161–166.Google Scholar
13.Partsinevelos, P., Stefanidis, A. and Agouris, P., “Automated Spatiotemporal Scaling for Video Generalization,” Proceedings of the International Conference on Image Processing 1 2001 pp. 177180.Google Scholar
14.Gonzalez, R. G. and Woods, R. E., Digital Image Processing (Addison-Wesley, Boston, MA, 1993 pp. 47–51.Google Scholar
15.Berg, R. F., “Estimation and prediction for maneuvering target trajectories,” IEEE Trans. Autom. Control AC-38, 294304 1983.CrossRefGoogle Scholar
16.Lavalle, S. M. and Sharma, R., “On motion planning in changing partially predictable environments,” Int. J. Robot. Res. 16 (6), 705805 1997.CrossRefGoogle Scholar
17.Feddema, J. T. and Lee, C. S. G., “Active image feature prediction and control for visual tracking with a hand-eye coordinated camera,” IEEE Trans. Syst. Man Cybern. 20, 11721183 1990.CrossRefGoogle Scholar
18.Hashimoto, K., Kimoto, T., Ebine, T. and Kimura, H., “Manipulator Control With Image-Based Visual Servo,” Proceedings of the IEEE International Conference on Robotics and Automation 1991 pp. 2267–2272.Google Scholar
19.Kohonen, T., “Self-organized formation of topologically correct feature maps,” Biol. Cybern. 59–60 1982.Google Scholar
20.Shi, K. L., Chan, T. F., Wong, Y. K. and Ho, S. L., “Speed estimation of an induction motor drive using an optimized extended Kalman filter,” IEEE Trans. Ind. Electron. 49 (1), 124133 2002.CrossRefGoogle Scholar
2
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Trajectory estimation of a moving object using Kalman filter and Kohonen networks
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Trajectory estimation of a moving object using Kalman filter and Kohonen networks
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Trajectory estimation of a moving object using Kalman filter and Kohonen networks
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *