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An Overview of Intelligent Moving Machines (IMM)

by D.K. Chaturvedi, Amit Yadav
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 3
Year of Publication: 2015
Authors: D.K. Chaturvedi, Amit Yadav
10.5120/ijca2015906479

D.K. Chaturvedi, Amit Yadav . An Overview of Intelligent Moving Machines (IMM). International Journal of Computer Applications. 128, 3 ( October 2015), 20-27. DOI=10.5120/ijca2015906479

@article{ 10.5120/ijca2015906479,
author = { D.K. Chaturvedi, Amit Yadav },
title = { An Overview of Intelligent Moving Machines (IMM) },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 3 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number3/22853-2015906479/ },
doi = { 10.5120/ijca2015906479 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:20:17.965837+05:30
%A D.K. Chaturvedi
%A Amit Yadav
%T An Overview of Intelligent Moving Machines (IMM)
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 3
%P 20-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The intelligent moving machine is design for fast response, cost effective and obstacle avoiding in its path. The introduction of intelligent behavior of moving machine can adapt the change taken place in the environment and or operating conditions etc. This adaptive behavior of moving machine makes it more robust and useful for industrial applications. The paper deals with an overview of Intelligent Moving Machine (IMM) based on two broad category. Firstly, overview based on applications of intelligent moving machine and secondly, technique based intelligent moving machine.

References
  1. Gavrila.D.M, “The visual analysis of intelligent machine movement: a survey. Digital Image Understand 73(1): pp.82-98, 1999.
  2. Gavrila.D.M and Philomin.V, “Real-time object detection for smart vehicles”, in IEEE International Conference on Computer Vision, 1999.
  3. Moreno.J and Dapena.P , “ Path quality measure for sensor-based motion planning”. Robotics and Autonomous systems, vol . 44, pp. 131-150, 2003.
  4. Spinello.L and Arras.A, “People Detection in RGB-D Data”, in Proc. of IROS, pp.3838-3843, 2011.
  5. Welch.G and Bisho.H , “An Introduction to the Kalman Filter”, UNC-chapel, TR 95-041, 2006.
  6. Satake.J and Miura.J, “Person Following of a Moving Machine using Stereo Vision”, Journal of the Machine Society of Japan, vol.28, no.9, pp.1091-1099, 2010.
  7. Song.G, Wei.Z and Song.A, “A hybrid sensor network system for home monitoring applications”, IEEE Trans Consumer Electronics, vol.53, no.4, pp.143414, 2007.
  8. Patel.H and wankhade.M, “Human tracking in video surveillance” International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, December 2011.
  9. Haralick .R.M and Shapiro.S.M, Computer and Machine Vision, Addison-Wesley, 1993.
  10. Jung.J and Sukhatne G.S , “Detecting moving objects using a single camera on a moving machine in an outdoor environment”, In: Proc. Of International Conference on Intelligent Autonomous Systems, pp.980-987, 2004.
  11. Ryan.J, Zennaro.M, and Howell.A, “An overview of emerging results in cooperative MAV control”, Proc. Of 43rd IEEE Conference on Decision and Control, vol. 1, pp.602-607, 2007.
  12. B. Ilias , R. Nagarajan , and M. Murugappan , “Hospital nurse following machine :hardware development and sensor integration . International Journal of Medical Engineering and Informatics (IJMEI) 6(1): 1-13, Inderscience, ISSN; 1755-0653 (print), 1755-0661 (online), 2014.
  13. Messelodi .S, Modena.C.M and Zanin.M , “A computer vision system for the detection and classification of vehicles at urban road intersections , Pattern Anal Appl 8 (1-2) : pp. 42-56 , 2005
  14. Fritsch. J, Kleinehagenbrock. J, and Lang. S, “Multi-modal anchoring for human-machine attraction”, Robotics and Autonomous systems, vol.43 , pp.133-147 , 2003.
  15. Lee.J , Tsubouchi.T , Yamamoto.K , & Egawa.S , “People tracking using a machine in motion with laser range finder , Proc. Of the 2006 IEEE / RSJ International Conference on Intelligent Machine and Systems (IROS2006) , pp. 2396-2942 , Beijing , China , Oct 2006.
  16. Pratiha. D.K, Deb. K, and Ghosh .A, “Optimal path and gait generationSimultaneously of a six-legged moving machine using a GA-fuzzy approval”, Robotics and Autonomous systems Journal, vol.41, issue1, pp.1-20, 2002.
  17. S.J. Kim, W.K. Choi, and H.T. Jeon, “Intelligent machine control with personal digital assistants using Fuzzy logic and Neural network, in: Knowledge-Based Intelligent Information and Engineering Systems, Pt 3, Proc. vol. 3215 , pp.589-595, 2004.
  18. Marti.M.J , Foster.F and Wahi.N, “Multi-sensor human tracking”, Univ. of New South Wales course project report. 2008.
  19. Adam.A, Rivlin.E , and Shimshoni.I , “ Computing the sensory uncertainty field of a Vision-based localization sensor ,” proc . Of the IEEE International Conference on Robotics & Automation, pp. 2993-2999, April 2000.
  20. Franz, M.O and Krapp , H.G., “Wide-field motion-sensitive neurons and Matched filters for Optic flow fields”. Biological Cybernetics, 83: 185-197, 2000.
  21. Lee.J.H , Appenzeller.G , and Hashimoto.H , “ An agent for intelligent spaces : Functions and roles of machine intelligent in sensored , networked , thinking spaces, IEEE conference Intelligent Transportation Systems, Boston, pp. 983-988, 1997.
  22. Lee.Z.H and Hashimoto.H, “Intelligent Machine control by distributed sensors,” proc. Of IFAC Workshop Moving Machine Technology, pp. 85-90, 2001.
  23. Xu .F and Fujimara.K , “Human Detection using Depth and Gray images”Proc.of AVSS , pp.115-121 , 2003.
  24. Tomasi, C. and Kanade, T., “Shape and motion from image streams under orthography”. International Journal of Computer Vision, 9(2) :137-154, 1992
  25. Lindstrom.M and Eklundh.E , “ Detecting and tracking moving objects from a mobile Platform using a laser range scanner”, in Proc. IEEE / RSJ Int. conf. Intell Robots Syst ,Mani , HI , vol.3 , pp. 1364-1369 , 2001.
  26. Coaniciu. D, Ramesh. V and Meer. P, “Kernel-based object tracking”on Pattern Analysis and Machine Intelligent, vol.25 no.5, pp.564-577, May 2003.
  27. Gavrila D.M, “The visual analysis of human movement”. Computer Vision and Understanding, 82-98, 1999.
  28. Khati. O, “Real-time obstacle avoidance for manipulators and moving machine”, International Journal of Moving Machine Research, vol.5 no.1, pp.90-98 , 1986.
  29. Moeslund, T.B. and Granum , E., “A survey of computer vision-based human motion capture”. Computer Vision and Image Understanding, 81: 231-268 , 2001.
  30. Barraquand. J and Latombe .J.C, “Machine motion planning: a distributed representation Approach”, International Journal of Robotics Research, vol.10 no.6, pp. 628-649, 2006.
  31. Nelson.C and Polona.P ,“ Detection and recognition of periodic , non-rigi motion. Int. J Comput vis 23 , 261-282 , 1997.
  32. Han. H.K , Kim .B.K , and Lee. M , “ Active calibration of the machine / camera pose Using the circular objects,” Trans. on Control , Automation and Systems Engineering (in Korean) , vol.5 , no.3 , pp. 314-323 , April , 1999.
  33. Ramanan.D and Forsyth.A, “Finding and tracking people from the bottomup”, in Proceeding of Computer Vision and Pattern Recognition (CVPR), Madison Wisconnin , June 2003
  34. Beymer.D and Konolig.K , “Tracking obstacle from a moving platform. Int. Proceeding of 202 int. Symp. On Experimental Machines. pp. 234-244, 2002.
  35. Romdhani.S et al.: Computationally Efficient Face Detection, Proceedings of the 8th International Conference on Computer Vision, 2, p.2001.
  36. Jefferies. M. E, Baker. J, and Weng . W, “Machine IntelligentCognitive Mapping: A role for a global metric map in a cognitive process”. In Workshop on Machine and Cognitive Approaches to Spatial Mapping, 2003
  37. Sandeep Bhatia, Ajay Mudgal, and Amita Soni, “Alive Human Detection Using an Autonomous Mobile Rescue Robot”, Department of Electrical & Electronics, PEC University of Technology, Chandigarh, India vol.no.2, July 2010.
  38. Awad. F and Shamroukh.R , “Human Detection by Intelligent Machine Urban Search and Rescue Using Image Processing and Neural Networks” International Journal of Intelligent Science , pp.4 39-53, 2014.
  39. Burridge.R and Graham, “Providing machine assistance during extra-vehicular activity”, SPIE, 2001.
  40. Perrone J.A. and Thiele.A, “A model of speed taning in M T neurons”. Vision Research, 42(8): 1035-1051, 2002. Patnaik.S, Jain.S, and Tzafesta.Ss, “Innovations in intelligent machine and control”, Springer, 2006.
  41. Topp.E.A and Christensen.H.I, “Tracking for following and passing persons”, in Proc. IEEE / RSJ Int. conf. Intell. Machine Intelligent Syst., Edmonton, A.B, Canada, pp.710-715, 2005.
  42. Borenstein.J and Koren.K, “Obstacle Avoidance with Ultrasonic Sensors”. IEEE Journal of Robotics and Automation, vol. RA-4, No.2, pp. 213-218, 1988.
  43. Luo.R.C , Chen.Y , and Liao.C.T,“Mobile robot based human detection and tracking using range and intensity data fusion”, in Proc. IEEE Workshop Adv. Robot. Social Impacts, pp. 1-6, 2007.
  44. Okusako.O , and Sakane.S , “Human tracking with a Intelligent machine using a laser range- Finder . Journal of Robotics Society of Japan, vol.24 no.5, pp. 605-613 , July 2006.
  45. Borenstein .J, and Koren. K, “The vector field histogram-fast obstacle avoidance for mobile robots, IEEE Trans. on Robotics and Automation, pp.501-518, 1991.
  46. Jones. M .: Statistical Color Models with Application to skin Detection, IEEE Conference on Computer Vision and Pattern Recognition, 1, p. 274-280, 1999.
  47. Urzelai.J and Ezkerra.M, “Fuzzy controller for wall-following with a non-holonomous mobile robot” FUZZY-IEEE / IFES, BARCELONA, July, 1997.
  48. Mataric .Maja.J, “The Robotics Primer, MIT Press, Cambridge P.300, 2007.
  49. Horn. B and Schunck. B, “Determining Optical Flow”. Artificial Intelligence, 17: 185- 203, 1981
  50. Graham .J and Shillcutt. K, “Robot tracking of human subjects in field environment”, Proceedings of the International Symposium on Artificial Intelligence, Robotics, and Automation in space, 2003.
  51. Wang.L , Tang.T , Ning .H and Hu.W , “Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12) : pp.1505-1518 , 2003.
  52. Borenstein. J and Koren. K , “Real- time obstacle avoidance for fast moving machine”, IEEE Transactions on system , Man and Cybernetics B , vol.19 no.5 pp. 1179-1187 , 1989.
  53. Motai.Y , Visual-based human-machine interaction for extracting salient features of An industrial object for an automated assembly system, special issue on Machine Vision, International Journal of Computers in Industry 56 (8-9) pp.943-957, 2005.
  54. Wolfson.J.H, “On curve matching IEEE Trans. Pattern Anal Mach Intell 12, pp.483-489, 1990.
  55. Rastogi.A.K , Chatterji.B.N , Ray A.K . “Design of a real-time tracking system for fast-moving objects IETE J. Res 43, pp.359-369, 1997.
  56. Beymer.DandKonolige.K,Trackingobjectfromamobileplatform. In IJCAI-2001 Workshop on Reasoning with Uncertainty in Machine, Seattle, USA, 2001.
  57. Latombe .J.C, Machine Motion Planning , Kluwer Academic Publishers , Boston , MA ; 1991.
  58. Perez.L.G, Alegre.M , Ribeiro.A , “ Perception and tracking of dynamic objects For optimization of avoidance strategies in autonomous piloting of vehicles”, Paper Presented at International Conference Spatial Cognition , pp.500-519 , 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Kinect sensor Motor Drive Hardware Implementation Softcomputing approach.

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