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

Year 2016, Volume: 4 Issue: 4, 95 - 102, 06.12.2016

Emine Dogru Bolat Serdar Solak Adem Tuncer Mehmet Yildirim

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

  • 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.
  • 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
  • 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
  • N. Ohnishi, A. Imiya, “Independent component analysis of
  • optical flow for robot navigation,” Neurocomputing, vol. 71, no. 10-12, pp. 2140-2163, 2008. doi: 10.1016/j.neucom.2007.09.015
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • H. Qu, K. Xing, T. Alexander, “An improved genetic algorithm with
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Beagleboard-xm rev C system reference manual, Revision 1.0, April 4, 2010. [Online]. Available: http://beagleboard.org/static/BBxMSRM_latest.pdf
  • OpenCV 2.4.12.0 documentation, OpenCV API Reference, Introduction, 2015. [Online]. Available: http://docs.opencv.org/2.4/modules/core/doc/intro.html
  • 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
  • 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

Year 2016, Volume: 4 Issue: 4, 95 - 102, 06.12.2016

Emine Dogru Bolat Serdar Solak Adem Tuncer Mehmet Yildirim

Abstract

References

  • 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.
  • 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
  • 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
  • N. Ohnishi, A. Imiya, “Independent component analysis of
  • optical flow for robot navigation,” Neurocomputing, vol. 71, no. 10-12, pp. 2140-2163, 2008. doi: 10.1016/j.neucom.2007.09.015
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • H. Qu, K. Xing, T. Alexander, “An improved genetic algorithm with
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Beagleboard-xm rev C system reference manual, Revision 1.0, April 4, 2010. [Online]. Available: http://beagleboard.org/static/BBxMSRM_latest.pdf
  • OpenCV 2.4.12.0 documentation, OpenCV API Reference, Introduction, 2015. [Online]. Available: http://docs.opencv.org/2.4/modules/core/doc/intro.html
  • 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
  • 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
There are 40 citations in total.

Details

Journal Section Research Article
Authors

Emine Dogru Bolat

Serdar Solak

Adem Tuncer

Mehmet Yildirim

Publication Date December 6, 2016
Published in Issue Year 2016 Volume: 4 Issue: 4

Cite

APA Dogru Bolat, E., Solak, S., Tuncer, A., Yildirim, M. (2016). A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments. International Journal of Intelligent Systems and Applications in Engineering, 4(4), 95-102.
AMA Dogru Bolat E, Solak S, Tuncer A, Yildirim M. A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments. International Journal of Intelligent Systems and Applications in Engineering. December 2016;4(4):95-102.
Chicago Dogru Bolat, Emine, Serdar Solak, Adem Tuncer, and Mehmet Yildirim. “A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments”. International Journal of Intelligent Systems and Applications in Engineering 4, no. 4 (December 2016): 95-102.
EndNote Dogru Bolat E, Solak S, Tuncer A, Yildirim M (December 1, 2016) A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments. International Journal of Intelligent Systems and Applications in Engineering 4 4 95–102.
IEEE E. Dogru Bolat, S. Solak, A. Tuncer, and M. Yildirim, “A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments”, International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 4, pp. 95–102, 2016.
ISNAD Dogru Bolat, Emine et al. “A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments”. International Journal of Intelligent Systems and Applications in Engineering 4/4 (December 2016), 95-102.
JAMA Dogru Bolat E, Solak S, Tuncer A, Yildirim M. A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments. International Journal of Intelligent Systems and Applications in Engineering. 2016;4:95–102.
MLA Dogru Bolat, Emine et al. “A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments”. International Journal of Intelligent Systems and Applications in Engineering, vol. 4, no. 4, 2016, pp. 95-102.
Vancouver Dogru Bolat E, Solak S, Tuncer A, Yildirim M. A Low Cost Single Board Computer Based Mobile Robot Motion Planning System for Indoor Environments. International Journal of Intelligent Systems and Applications in Engineering. 2016;4(4):95-102.