A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments

Abstract

In this study, a low cost, flexible and modular structure is proposed for mobile robot motion planning systems in an indoor environment with obstacles. In this system, the mobile robot has to follow the shortest path to the target avoiding obstacles. It is designed as three main modules including image processing, path planning and robot motion blocks. These modules are embedded on a single board computer. In the image processing module, the image of the indoor environment, including a mobile robot, obstacles and a target, having different colors is taken to the single board computer with a wireless IP camera. This image is processed to find the locations of the mobile robot, obstacles and the target in C programming language using OpenCV. In path planning module, the shortest and optimal path is generated for the mobile robot. Generated path is applied to the robot motion module to produce necessary angles and distances for the mobile robot to reach the target. Since the structure of the proposed system is designed as modular and flexible, similar or different hardware, software or methods can be applied to these three modules.

Keywords

• Genetic Algorithms,image color analysis; mobile robots; open source software; open source hardware; path planning

References

1. F. Bonin-Font, A. Ortiz, G. Oliver, “Visual navigation for mobile robots: a survey,” Journal of Intelligent and Robotic Systems, vol. 53, pp. 263-296, 2008. [Online]. Available: http://dmi.uib.es/~fbonin/survey.pdf.
2. A. Fernández-Caballero, J. C. Castillo, J. Martínez-Cantos, R. Martínez-Tomás, “Optical flow or image subtraction in human detection from infrared camera on mobile robot,” Robotics and Autonomous Systems, vol. 58, no. 12, pp. 1273-1281, 2010. doi:10.1016/j.robot.2010.06.002
3. N. Ohnishi, A. Imiya, “Appearance-based Navigation and Homing for Autonomous Mobile Robot,” Image and Vision Computing, vol. 31, no. 6-7, pp. 511-532, 2013. doi: 10.1016/j.imavis.2012.11.004
4. N. Ohnishi, A. Imiya, “Independent component analysis of
5. optical flow for robot navigation,” Neurocomputing, vol. 71, no. 10-12, pp. 2140-2163, 2008. doi: 10.1016/j.neucom.2007.09.015
6. R. Abiyev R, D. Ibrahim, B. Erin, “Navigation of mobile robots in the presence of obstacles,” Advances in Engineering Software, vol. 41, no. 10-11, pp. 1179-1186, 2010. doi: 10.1016/j.advengsoft.2010.08.001
7. K. Samsudin, F. A. Ahmad, S. Mashohor, “A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot,” Applied Soft Computing, vol. 11, no. 2, pp. 1631-1637, 2011. doi: 10.1016/j.asoc.2010.05.002
8. W. H. Huang, B. R. Fajen, J. R. Fink, W. H. Warren, “Visual navigation and obstacle avoidance using a steering potential function,” Robotics and Autonomous Systems, vol. 54, no. 4, pp. 288-299, 2006. doi: 10.1016/j.robot.2005.11.004

9. J. Agirrebeitia, R. Avilés, I. F. Bustos, G. Ajuria, “A new APF strategy for path planning in environments with obstacles,” Mechanism and Machine Theory, vol. 40, no. 6, pp. 645-658, 2005. doi: 10.1016/j.mechmachtheory.2005.01.006
10. V. Sezer, M. Gokasan, “A novel obstacle avoidance algorithm: follow the gap method,” Robotics and Autonomous Systems, vol. 60, no. 9, pp. 1123-1134, 2012. doi: 10.1016/j.robot.2012.05.021
11. M. Duguleana, F. G. Barbuceanu, A. Teirelbar, G. Mogan, “Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning,” Robotics and Computer-Integrated Manufacturing, vol. 28, no. 2, pp. 132-146, 2012. doi: 10.1016/j.rcim.2011.07.004
12. E. G. Gilbert, D. W. Johnson, “Distance functions and their application to robot path planning in the presence of obstacles,” IEEE Journal of Robotics and Automation, vol. 1, no. 1, pp. 21-30, 1985. doi: 10.1109/JRA.1985.1087003
13. J. Borenstein, Y. Koren, “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 278-288, 1991. doi: 10.1109/70.88137
14. J. Guo, Z. Shouping, X. Jia, Z. Shengui, “Kalman prediction based VFH of dynamic obstacle avoidance for intelligent vehicles,” in Proc. IEEE 2010 Computer Application and Systems Modeling Conf., Chengdu Sichuan, 2010, pp. 6-10. [Online]. Available:http://dx.doi.org/10.1109/ICCASM.2010.5620252
15. I. Ulrich, J. Borenstein, “VFH+: Reliable obstacle avoidance for fast mobile robots,” in Proc. IEEE 1998 Robotics and Automation Conf., Leuven, 1998, pp. 1572-1577. [Online]. Available: http://dx.doi.org/10.1109/ROBOT.1998.677362
16. P. Saranrittichai, N. Niparnan, A. Sudsang, “Robust local obstacle avoidance for mobile robot based on dynamic window approach,” in Proc. IEEE 2013 International Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology Conf., Krabi, 2013, pp. 1-4. [Online]. Available: http://dx.doi.org/10.1109/ECTICon.2013.6559615
17. P. Ogren, N. E. Leonard, “A Convergent dynamic window approach to obstacle avoidance,” IEEE Transactions on Robotics, vol. 21, no. 2, pp. 188-195, 2005. doi: 10.1109/TRO.2004.838008
18. X. Rulong, W. Qiang, S. Lei, L. Lei, “Design of multi-robot path planning system based on hierarchical fuzzy control,” Procedia Engineering, vol. 15, pp. 235-239, 2011. doi: 10.1016/j.proeng.2011.08.047
19. A. Tuncer, M. Yildirim, “Dynamic path planning of mobile robots with improved genetic algorithm,” Computers and Electrical Engineering, vol. 38, no. 6, pp. 1564-1572, 2012. doi: 10.1016/j.compeleceng.2012.06.016
20. H. Qu, K. Xing, T. Alexander, “An improved genetic algorithm with
21. co-evolutionary strategy for global path planning of multiple mobile robots,” Neurocomputing, vol. 120, pp. 509-517, 2013. doi: 10.1016/j.neucom.2013.04.020
22. A. Tuncer, M. Yildirim, K. Erkan, “A Motion planning system for mobile robots,” Advances in Electrical and Computer Engineering, vol. 12, no. 1, pp. 57-62, 2012. doi: 10.4316/AECE.2012.01010
23. J.C. Mohanta, D. R. Parhi, S.K. Patel, “Path planning strategy for autonomous mobile robot navigation using petri-ga optimization,” Computers and Electrical Engineering, vol. 37, no. 6, pp. 1058-1070, 2011. doi: 10.1016/j.compeleceng.2011.07.007
24. Q. Zhu, J. Hu, L. Henschen, “A new target interception algorithm for mobile robots based on sub-goal forecasting and an improved scout ant algorithm,” Applied Soft Computing, vol. 13, no. 1, pp. 539-549, 2013. doi: 10.1016/j.asoc.2012.08.013
25. E. Masehian, D. Sedighizadeh, “Multi-objective PSO- and NPSO-based algorithms for robot path planning,” Advances in Electrical and Computer Engineering, vol. 10, no. 4, pp. 69-76, 2010. doi: 10.4316/AECE.2010.04011
26. C. Yu, Q. Qiu, X. Chen, “A hybrid two-dimensional path planning model based on frothing construction algorithm and local fast marching method,” Computers and Electrical Engineering, vol. 39, no. 2, pp. 475-487, 2013. doi: 10.1016/j.compeleceng.2012.09.010
27. A.N. Asl, M.B. Menhaj, A. Sajedin, “Control of leader-follower formation and path planning of mobile robots using asexual reproduction optimization (ARO),” Applied Soft Computing, vol. 14, pp. 563-576, 2014. doi: 10.1016/j.asoc.2013.07.030
28. B. Seckin, T. Ayan, E. Germen, “Autopilot project with unmanned robot,” Procedia Engineering, vol. 41, pp. 958-964, 2012. doi: 10.1016/j.proeng.2012.07.269
29. M. Yamada, C. Lin, M. Cheng, “Vision based obstacle avoidance and target tracking for autonomous mobile robots,” in Proc. IEEE 2010 International Workshop on Advanced Motion Control, Nagaoka, 2010, pp. 153-158. [Online]. Available: http://dx.doi.org/10.1109/AMC.2010.5464007
30. S. Liu, D. Sun, C. Zhu, “A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming,” Robotics and Computer-Integrated Manufacturing, vol. 30, no. 6, pp. 589-596, 2014. doi: 10.1016/j.rcim.2014.04.002
31. H. Aliakbarpour, O. Tahri, H. Araujo, “Visual servoing of mobile robots using non-central catadioptric cameras,” Robotics and Autonomous Systems, vol. 62, no. 11, pp. 1613-1622, 2014. doi:10.1016/j.robot.2014.03.007
32. A.V. Savkin, C. Wang, “Seeking a path through the crowd: Robot navigation in unknown dynamic environments with moving obstacles based on an integrated environment representation,” Robotics and Autonomous Systems, vol. 62, no. 10, pp. 1568-1580, 2014. doi: 10.1016/j.robot.2014.05.006
33. X. Zhong, X. Zhong, X. Peng, “Velocity-Change-Space-based dynamic motion planning for mobile robots navigation,” Neurocomputing, vol. 143, pp. 153-163, 2014. doi: 10.1016/j.neucom.2014.06.010
34. D.O. Sales, D.O. Correa, L.C. Fernandes, D.F. Wolf, F.S. Osório, “Adaptive finite state machine based visual autonomous navigation system,” Engineering Applications in Artificial Intelligence, vol. 29, pp. 152-162, 2014. doi: 10.1016/j.engappai.2013.12.006
35. A. Tuncer, M. Yildirim, K. Erkan, “A motion planning system for mobile robots,” Advances in Electrical and Computer Engineering, vol. 12, no. 1, pp. 57-62, 2012. doi: 10.4316/AECE.2012.01010
36. S. Solak, E. D. Bolat, “Real time industrial application of single board computer based color detection system,” in Proc. IEEE 2013 Electrical and Electronics Engineering Conf., Bursa, 2013, pp. 353-357. [Online]. Available: http://dx.doi.org/10.1109/ELECO.2013.6713860
37. Beagleboard-xm rev C system reference manual, Revision 1.0, April 4, 2010. [Online]. Available: http://beagleboard.org/static/BBxMSRM_latest.pdf
38. OpenCV 2.4.12.0 documentation, OpenCV API Reference, Introduction, 2015. [Online]. Available: http://docs.opencv.org/2.4/modules/core/doc/intro.html
39. L. M. Russo, E. C. Pedrino, E. Kato, V.O. Roda, “Image convolution processing: a GPU versus FPGA comparison,” in Proc. IEEE 2012 Southern Conf. on Programmable Logic, Bento Gonçalves, 2012, pp. 1-6. [Online]. Available: http://dx.doi.org/10.1109/SPL.2012.6211783
40. J. F. Pekel, C. Vancutsem, L. Bastin, M. Clerici, E, Vanbogaert, E. Bartholomé, P. Defourny, “A near real-time water surface detection method based on HSV transformation of modis multi-spectral time series data,” Remote Sensing Environment, vol. 140, pp. 704-716, 2014. doi: 10.1016/j.rse.2013.10.008