Path Planning Based on EMD and Partial Distance Feature Vector Matching with Fuzzy Logic for Indoor Robotic

Abstract:

Path planning algorithm is an important research content of robotic. In order to improve its performance, we use earth move’s distance(EMD) and fuzzy logic as base tools, combine with distance feature vector matching and partial matching tech，by use the algorithms based on EMD and feature vector, we can get more faster and more accurate outcome, and at the same time, the algorithm could decrease the increase velocity of the search space from the scale of secondary power into the the scale of linearity.

• Periodical: Advanced Materials Research (Volume 684)
• Pages: 456-459
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.684.456
• Online since: April 2013
• Authors: Zhi Jie Zhang, Shuo Yang
• Keywords: Distance Feature Vector, Earth Move’s Distance, Fuzzy Logic, Path Planning

References

[1] E Gilbert, D Johnson. Distance functions and their application to robot path planning in the presence of obstacles. IEEE Journal of Robotics and Automation, 1985, Issue 1 of Vol 1: 21 – 30.

DOI: 10.1109/jra.1985.1087003

[2] A. Zelinsky R.A. Jarvis,J. Byrne,S. Yuta. Planning paths of complete coverage of an unstructured environment by a mobile robot. International Conference on Advanced Robotics, Tokyo, Japan, 1993: 533–538.

[3] H Choset. Coverage for robotics – A survey of recent results. Annals of Mathematics and Artificial Intelligence, Numbers 1-4, Vol 31, 2001: 113-126.

[4] T Oksanen. Coverage path planning algorithms for agricultural field machines. A Visa la - Journal of Field Robotics; Issue 8, Vol26, Aug 2009: 651–668.

DOI: 10.1002/rob.20300

[5] R.A. Brooks. A robust layered control system for a mobile robot. IEEE J. Robotics Autom, (1986).

[6] Ismail AL-Taharwa, Alaa Sheta and Mohammed Al-Weshah. A mobile robot path planning using genetic algorithm in static environment. Journal of Computer Science 4 . 2008: 341-344.

DOI: 10.3844/jcssp.2008.341.344

[7] Ersson,T., and Hu. Path planning and navigation of mobile robots in unknown environments. IEEE International Conferenceon on Intelligent Robots and Systems(IROS). (2001).

DOI: 10.1109/iros.2001.976276

[8] Q Wu, Y Yu. Feature matching and deformation for texture synthesis. ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH, Issue 3, Vol 23, Aug 2004: 364-367.

DOI: 10.1145/1015706.1015730

[9] C Schutz, T Jost, H Hugli. Multi-feature matching algorithm for free-form 3D surface registration. Fourteenth International Conference on Pattern Recognition, Aug 1998，vol. 2 : 982 – 984.

DOI: 10.1109/icpr.1998.711852

[10] Y Chen, JZ Wang. A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, Sep 2002, Issue 9 , Vol 24: 1252 – 1267.

DOI: 10.1109/tpami.2002.1033216

[11] Y Chen, JZ Wang. Looking beyond region boundaries: A robust image similarity measure using fuzzified region features. The 12th IEEE International Conference on Fuzzy Systems, May 2003, vol. 2 : 1165 – 1170.

DOI: 10.1109/fuzz.2003.1206596

[12] M Wang, JNK Liu. Fuzzy logic based robot path planning in unknown environment. International Conference on Machine Learning and Cybernetics, Aug. 2005, vol. 2: 813-818.

DOI: 10.1109/icmlc.2005.1527055

[13] G Antonelli, S Chiaverini. A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking. Fuzzy Systems, IEEE Transactions on April . 2007, Issue 2 , Vol 15 : 211 – 221.

DOI: 10.1109/tfuzz.2006.879998