Vision based trajectory following robot and swarm

Ishraq Ul Haque; Abul Al Arabi; Shadat Hossain; Tanjina Proma; Nafi Uzzaman; M Ashraful Amin

doi:10.1109/iccre.2017.7935037

Conference Paper2017Cited by 5

Vision based trajectory following robot and swarm

Ishraq Ul Haque, Abul Al Arabi, Shadat Hossain, Tanjina Proma, Nafi Uzzaman, M Ashraful Amin

2017 2nd International Conference on Control and Robotics Engineering (ICCRE)

IEEE, pp. 35–38

DOI: 10.1109/iccre.2017.7935037 URL

Abstract

Robots based on vision guidance are very important to the future of vehicle navigation. They have the ability to navigate through a predetermined track and avoid obstacles. They operate remotely with a central computer tracking and guiding them. This allows for communication between robots. This can be used to implement traffic based navigation. Where routes can be recalculated depending on traffic flow. Use of server based system paves the way to collaborate among different robots.

Keywords

RobotTrajectoryComputer scienceSwarm behaviourTrack (disk drive)Artificial intelligenceMobile robotTracking (education)Real-time computingComputer visionSimulationHuman–computer interaction

CCDS Authors

Prof. M Ashraful Amin, PhD

Founder Director, CCDS
Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

1.Francesco Mondada, Luca Maria Gambardella, Dario Floreano, Stefano Nolfi, J. L. Deneubourg, Marco Dorigo. (2005). The cooperation of swarm-bots - Physical interactions in collective robotics. IEEE Robotics & Automation Magazine, 12(2), 21–28[10.1109/mra.2005.1458313]
2.Philip Kahn, Les Kitchen, Edward M. Riseman. (1990). A fast line finder for vision-guided robot navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(11), 1098–1102[10.1109/34.61710]
3.Arindam Das, Rafael Fierro, Vijay Kumar, B. Southall, John Spletzer, Camillo J. Taylor. (2002). Real-time vision-based control of a nonholonomic mobile robot. , 2, 1714–1719[10.1109/robot.2001.932858]
4.Abdul Halim Ismail, Hafiz Rashidi Ramli, M. H. Ahmad, Mohammad Hamiruce Marhaban. (2009). Vision-based system for line following mobile robot. , 642–645[10.1109/isiea.2009.5356366]
5.G. Beccari, Stefano Caselli, Francesco Zanichelli, A. Calafiore. (2002). Vision-based line tracking and navigation in structured environments. , 406–411[10.1109/cira.1997.613888]
6.K. Horak and L. Zalud. (2016). Image Processing on Raspberry Pi for Mobile Robotics. International Journal of Signal Processing Systems, 494–498[10.18178/ijsps.4.6.494-498]
7.Aninda Saha, Mahady Hasan, MD ASHRAFUL, S. Sampath Kumar. (2015). Android Based Autonomous Arduino Bot. , 34–38[10.15224/978-1-63248-056-9-26]
8.J. Christian Dupuis, Marc Parizeau. (2006). Evolving a Vision-Based Line-Following Robot Controller. , 75[10.1109/crv.2006.32]
9.Samreen Amir, Ali Akbar Siddiqui, Nimrah Ahmed, Bhawani Shankar Chowdhry. (2014). Implementation of line tracking algorithm using Raspberry pi in marine environment. , 1337–1341[10.1109/ieem.2014.7058856]
10.Miftahur Rahman, Md. Hasnaeen Rizvi Rahman, Abul L. Haque, M. Towhidul Islam. (2005). Architecture of the Vision System of a Line Following Mobile Robot Operating in Static Environment. , 1–8[10.1109/inmic.2005.334473]
11.Abul Al Arabi, Pranabesh Sarkar, Faysal Ahmed, Waliur Rahman Rafie, Mahfuz Hannan, M. Ashraful Amin. (2017). 2D mapping and vertex finding method for path planning in autonomous obstacle avoidance robotic system. , 39–42[10.1109/iccre.2017.7935038]
12.Larry Andres Perez Sanchez, Cesar Augusto Aceros Moreno, Holguer Andres Becerra Daza. (2015). Design and construction of a line follower robot guided by pixels values of a camera connected to an FPGA. , 1–5[10.1109/stsiva.2015.7330453]
13.Khondekar Mahabub Akram, M. M. Lutfe Elahi, M. Ashraful Amin. (2013). Multiple level set region based single line road extraction. , 1201–1206[10.1109/icmlc.2013.6890773]
14.Urs Ramer. (1975). Extraction of line structures from photographs of curved objects. Computer Graphics and Image Processing, 4(2), 81–103[10.1016/s0146-664x(75)80002-5]
15.Daniel C. Dennett. (2016). Where Am I?. , 55–68[10.1002/9781118922590.ch6]

Cited By

1.Sy-Hung Bach, Soo-Yeong Yi. (2022). An Efficient Approach for Line-Following Automated Guided Vehicles Based on Fuzzy Inference Mechanism. Journal of Robotics and Control (JRC), 3(4), 395–401[10.18196/jrc.v3i4.14787]
2.Ahmed Bendimrad, Ayoub El Amrani, Bouchta El Amrani. (2020). Design and implementation of line follower and obstacle detection robot. International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, 11(1), 160[10.11591/ijpeds.v11.i1.pp160-168]
3.Abul Al Arabi, Pranabesh Sarkar, Faysal Ahmed, Waliur Rahman Rafie, Mahfuz Hannan, M Ashraful Amin. (2017). 2D mapping and vertex finding method for path planning in autonomous obstacle avoidance robotic system. , 39–42[10.1109/iccre.2017.7935038]
4.Adil Hashim, Tanya Saini, Hemant Bhardwaj, Adityan Jothi, Ammannagari Vinay Kumar. (2018). Application of Swarm Intelligence in Autonomous Cars for Obstacle Avoidance. Studies in computational intelligence, 393–404[10.1007/978-981-10-8797-4_41]
5.Jannatul Nayeem, Mir Oliul Pasha Taj, Md. Shahria Mahmud, Farha Hossain, Abul Al Arabi. (2022). Implementation of Computer Vision in Low Fidelity Robots: Analysis, Challenges, and Design Implications. , 105, 339–344[10.1109/nics56915.2022.10013428]

← All publications

Abstract

CCDS Authors

Prof. M Ashraful Amin, PhD

Founder Director, CCDS
Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

1.Francesco Mondada, Luca Maria Gambardella, Dario Floreano, Stefano Nolfi, J. L. Deneubourg, Marco Dorigo. (2005). The cooperation of swarm-bots - Physical interactions in collective robotics. IEEE Robotics & Automation Magazine, 12(2), 21–28[10.1109/mra.2005.1458313]

2.Philip Kahn, Les Kitchen, Edward M. Riseman. (1990). A fast line finder for vision-guided robot navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(11), 1098–1102[10.1109/34.61710]

3.Arindam Das, Rafael Fierro, Vijay Kumar, B. Southall, John Spletzer, Camillo J. Taylor. (2002). Real-time vision-based control of a nonholonomic mobile robot. , 2, 1714–1719[10.1109/robot.2001.932858]

4.Abdul Halim Ismail, Hafiz Rashidi Ramli, M. H. Ahmad, Mohammad Hamiruce Marhaban. (2009). Vision-based system for line following mobile robot. , 642–645[10.1109/isiea.2009.5356366]

5.G. Beccari, Stefano Caselli, Francesco Zanichelli, A. Calafiore. (2002). Vision-based line tracking and navigation in structured environments. , 406–411[10.1109/cira.1997.613888]

6.K. Horak and L. Zalud. (2016). Image Processing on Raspberry Pi for Mobile Robotics. International Journal of Signal Processing Systems, 494–498[10.18178/ijsps.4.6.494-498]

7.Aninda Saha, Mahady Hasan, MD ASHRAFUL, S. Sampath Kumar. (2015). Android Based Autonomous Arduino Bot. , 34–38[10.15224/978-1-63248-056-9-26]

8.J. Christian Dupuis, Marc Parizeau. (2006). Evolving a Vision-Based Line-Following Robot Controller. , 75[10.1109/crv.2006.32]

9.Samreen Amir, Ali Akbar Siddiqui, Nimrah Ahmed, Bhawani Shankar Chowdhry. (2014). Implementation of line tracking algorithm using Raspberry pi in marine environment. , 1337–1341[10.1109/ieem.2014.7058856]

10.Miftahur Rahman, Md. Hasnaeen Rizvi Rahman, Abul L. Haque, M. Towhidul Islam. (2005). Architecture of the Vision System of a Line Following Mobile Robot Operating in Static Environment. , 1–8[10.1109/inmic.2005.334473]

11.Abul Al Arabi, Pranabesh Sarkar, Faysal Ahmed, Waliur Rahman Rafie, Mahfuz Hannan, M. Ashraful Amin. (2017). 2D mapping and vertex finding method for path planning in autonomous obstacle avoidance robotic system. , 39–42[10.1109/iccre.2017.7935038]

12.Larry Andres Perez Sanchez, Cesar Augusto Aceros Moreno, Holguer Andres Becerra Daza. (2015). Design and construction of a line follower robot guided by pixels values of a camera connected to an FPGA. , 1–5[10.1109/stsiva.2015.7330453]

13.Khondekar Mahabub Akram, M. M. Lutfe Elahi, M. Ashraful Amin. (2013). Multiple level set region based single line road extraction. , 1201–1206[10.1109/icmlc.2013.6890773]

14.Urs Ramer. (1975). Extraction of line structures from photographs of curved objects. Computer Graphics and Image Processing, 4(2), 81–103[10.1016/s0146-664x(75)80002-5]

15.Daniel C. Dennett. (2016). Where Am I?. , 55–68[10.1002/9781118922590.ch6]

Cited By

1.Sy-Hung Bach, Soo-Yeong Yi. (2022). An Efficient Approach for Line-Following Automated Guided Vehicles Based on Fuzzy Inference Mechanism. Journal of Robotics and Control (JRC), 3(4), 395–401[10.18196/jrc.v3i4.14787]

2.Ahmed Bendimrad, Ayoub El Amrani, Bouchta El Amrani. (2020). Design and implementation of line follower and obstacle detection robot. International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, 11(1), 160[10.11591/ijpeds.v11.i1.pp160-168]

3.Abul Al Arabi, Pranabesh Sarkar, Faysal Ahmed, Waliur Rahman Rafie, Mahfuz Hannan, M Ashraful Amin. (2017). 2D mapping and vertex finding method for path planning in autonomous obstacle avoidance robotic system. , 39–42[10.1109/iccre.2017.7935038]

4.Adil Hashim, Tanya Saini, Hemant Bhardwaj, Adityan Jothi, Ammannagari Vinay Kumar. (2018). Application of Swarm Intelligence in Autonomous Cars for Obstacle Avoidance. Studies in computational intelligence, 393–404[10.1007/978-981-10-8797-4_41]

5.Jannatul Nayeem, Mir Oliul Pasha Taj, Md. Shahria Mahmud, Farha Hossain, Abul Al Arabi. (2022). Implementation of Computer Vision in Low Fidelity Robots: Analysis, Challenges, and Design Implications. , 105, 339–344[10.1109/nics56915.2022.10013428]