Machine Vision System for Autonomous Guidance of Raisin-Collection Rover

Document Type : Research Article

Authors

1 Department of Biosystems Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

2 Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

The development of autonomous vehicles based on machine vision system in various industries and agriculture is a challenging topic. In this study, the active-contour algorithm was utilized for path-tracking of an automatic raisin-collecting rover. For this purpose, the energy difference between the background and foreground levels is utilized to extract the boundary between raisins and the ground surface with good accuracy which is used for navigating the path. This was accomplished by developing a small unmanned rover equipped with four DC motors and a machine vision system. Field tests at the Sunny Raisin Court showed that the system was able to independently detect and navigate its path with an RMS error of 1.57 cm. The results of analysis of variance for accuracy of the path tracking showed that the effect of lighting conditions and speed on the accuracy was significant at the level of one percent.

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[1] E.A. Oyekanlu, A.C. Smith, W.P. Thomas, G. Mulroy, D. Hitesh, M. Ramsey, D.J. Kuhn, J.D. Mcghinnis, S.C. Buonavita, N.A. Looper, A review of recent advances in automated guided vehicle technologies: Integration challenges and research areas for 5G-based smart manufacturing applications, IEEE Access, 8 (2020) 202312-202353.
[2] L. Sabattini, M. Aikio, P. Beinschob, M. Boehning, E. Cardarelli, V. Digani, A. Krengel, M. Magnani, S. Mandici, F. Oleari, The pan-robots project: Advanced automated guided vehicle systems for industrial logistics, IEEE Robotics & Automation Magazine, 25(1) (2017) 55-64.
[3] S. Dehghani, S.H. Karparvarfard, H. Rahmanian Koushkak, Design, development, and evaluation of an automatic guidance system for tractor tracking along the contour line on inclined surfaces, Agricultural Machinery, 6(6) (2016) 13.
[4] Y. Ji, J. Hwang, E.Y. Kim, An intelligent wheelchair using situation awareness and obstacle detection, Procedia-social and behavioral sciences, 97 (2013) 620-628.
[5] G. Harries, B. Ambler, Automatic ploughing: A tractor guidance system using opto-electronic remote sensing techniques and a microprocessor based controller, Journal of Agricultural Engineering Research, 26(1) (1981) 33-53.
[6] H. Olsen, Determination of row position in small-grain crops by analysis of video images, Computers and Electronics in Agriculture, 12(2) (1995) 147-162.
[7] J.V. Gomez, F.E. Sandnes, RoboGuideDog: guiding blind users through physical environments with laser range scanners, Procedia Computer Science, 14 (2012) 218-225.
[8] T. Chateau, C. Debain, F. Collange, L. Trassoudaine, J. Alizon, Automatic guidance of agricultural vehicles using a laser sensor, Computers and electronics in agriculture, 28(3) (2000) 243-257.
[9] P.S. Pratama, T.H. Nguyen, H.K. Kim, D.H. Kim, S.B. Kim, Positioning and obstacle avoidance of automatic guided vehicle in partially known environment, International Journal of Control, Automation and Systems, 14(6) (2016) 1572-1581.
[10] M.A. A, M. H, K. A, Designing the Diagnosis and Avoidance Algorithm of Obstacle Using the Lidar on a Robot Boat, Maritime Transport Industry, 3(4) (2017) 14.
[11] R. Patterson, B. Fehr, L. Sheets, Electronic guidance system for a planter, American Society of Agricultural Engineers (Microfiche collection)(USA),  (1985).
[12] S. Jang, K. Ahn, J. Lee, Y. Kang, A study on integration of particle filter and dead reckoning for efficient localization of automated guided vehicles, in:  2015 IEEE international symposium on robotics and intelligent sensors (IRIS), IEEE, 2015, pp. 81-86.
[13] A. Lawrence, Modern inertial technology-Navigation, guidance, and control, NASA STI/Recon Technical Report A, 93 (1993) 39795.
[14] M.T. Sabet, H.M. Daniali, A. Fathi, E. Alizadeh, Experimental analysis of a low-cost dead reckoning navigation system for a land vehicle using a robust AHRS, Robotics and Autonomous Systems, 95 (2017) 37-51.
[15] Q. Zhang, H. Qiu, A dynamic path search algorithm for tractor automatic navigation, Transactions of the ASAE, 47(2) (2004) 639.
[16] N. Powell, M. Boyette, A sensor integration method for autonomous equipment, in:  2005 ASAE Annual Meeting, American Society of Agricultural and Biological Engineers, 2005, pp. 17-20 .
[17] J. Long, C.L. Zhang, The summary of AGV guidance technology, in:  Advanced Materials Research, Trans Tech Publ, 591 (2012) 1625-1628.
[18] M. M, A.-F.M. H, A. M.H, The Possibility of positioning and automous guidance of agriculture vehicle with GPS data, in:  The 8th National Congress on Agr. Machinery Eng. (Biosystem) & Mechanization. , Mashhad, Iran, 2017.
[19] A. Mahdavian, S. Minaei, A. Banakar, Design, development, and evaluation of a fuzzy-based automatic guidance system for JD955 combine harvester, AMA, Agricultural Mechanization in Asia, Africa and Latin America, 50(3) (2019) 34-42.
[20] P. Ong, W. Kar ShenTan, E. SoongLow, Vision-based path detection of an automated guided vehicle using flower pollination algorithm, Ain Shams Engineering Journal,  12(2) (2020) 2263-2274
[21] E. Benson, J. Reid, Q. Zhang, Machine vision–based guidance system for an agricultural small–grain harvester, Transactions of the ASAE, 46(4) (2003) 1255.
[22] V.S. Chakra Kumar, A. Sinha, P.P. Mallya, N. Nath, An approach towards automated navigation of vehicles using overhead cameras, in:  Conf. Comput. Intell. Comput. Res. (ICCIC), Proc. IEEE Int, 2017, pp.1-8.
[23] J. Radcliffe, J. Cox, D.M. Bulanon, Machine vision for orchard navigation, Computers in Industry, 98 (2018) 165-171.
[24] S. Han, Q. Zhang, B. Ni, J. Reid, A guidance directrix approach to vision-based vehicle guidance systems, Computers and electronics in Agriculture, 43(3) (2004) 179-195.
[25] A. Roshanianfard, N. Noguchi, H. Okamoto, K. Ishii, A review of autonomous agricultural vehicles (The experience of Hokkaido University), Journal of Terramechanics, 91 (2020) 155-183.
[26] B. Shamah, M.D. Wagner, S. Moorehead, J. Teza, D. Wettergreen, W.L. Whittaker, Steering and control of a passively articulated robot, in:  Sensor Fusion and Decentralized Control in Robotic Systems IV, International Society for Optics and Photonics, 2001, pp. 96-107.
[27] H. Ramezani, H. ZakiDizaji, H. Masoudi, G. Akbarizadeh, A new DSWTS algorithm for real-time pedestrian detection in autonomous agricultural tractors as a computer vision system, Measurement, 93 (2016) 126-134.
[28] S. Kuutti, R. Bowden, Y. Jin, P. Barber, S. Fallah, A survey of deep learning applications to autonomous vehicle control, IEEE Transactions on Intelligent Transportation Systems, 22(2) (2020) 712-733.
[29] M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models, International journal of computer vision, 1(4) (1988) 321-331.
[30] D. Freedman, T. Zhang, Active contours for tracking distributions, IEEE Transactions on Image Processing, 13(4) (2004) 518-526 .
[31] J.S. Chang, E.Y. Kim, K. Jung, H.J. Kim, Real Time Hand Tracking Based on Active Contour Model, 3483 (2005).
[32]  S. Lefèvre, N. Vincent. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. (2004) , Real Time Multiple Object Tracking Based on Active Contours. , Image Analysis and Recognition, 3212 (2004).
[33] C. Shan, Y. Wei, T. Tan, F. Ojardias, Real time hand tracking by combining particle filtering and mean shift, In Sixth IEEE International Conference on Automatic Face and Gesture Recognition,  (2004) 669-674.