Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-10T14:59:32.110Z Has data issue: false hasContentIssue false

Maximum clearance rapid motion planning algorithm

Published online by Cambridge University Press:  19 February 2018

Shubham Singh Paliwal*
Affiliation:
Robotics and Artificial Intelligence laboratory, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India. E-mail: rkala001@gmail.com
Rahul Kala
Affiliation:
Robotics and Artificial Intelligence laboratory, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India. E-mail: rkala001@gmail.com
*
*Corresponding author. E-mail: shubhamrnq797@gmail.com

Summary

This paper proposes a new path-planning algorithm which is close to the family of bug algorithms. Path planning is one of the challenging problems in the area of service robotics. In practical applications, traditional methods have some limitations with respect to cost, efficiency, security, flexibility, portability, etc. Our proposed algorithm offers a computationally inexpensive goal-oriented strategy by following a smooth and short trajectory. The paper also presents comparisons with other algorithms. In addition, the paper also presents a test bed which is created to test the algorithm. We have used a two-wheeled differential drive robot for the navigation and only a single camera is used as a feedback sensor. Using an extended Kalman filter, we localize the robot efficiently in the map. Furthermore, we compare the actual path, predicted path and planned path to check the effectiveness of the control system.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Bender, M. A., Fernandez, A., Ron, D., Sahai, A. and Vadhan, S., “The Power of a Pebble: Exploring and Mapping Directed Graphs,” Proceedings of the Annual Symposium on Foundations of Computer Science (1998) pp. 1–2Google Scholar
2. Tiwari, R., Shukla, A. and Kala, R., Intelligent Planning for Mobile Robotics: Algorithmic Approaches (IGI-Global, Hershey, PA, 2013).CrossRefGoogle Scholar
3. Clarke, F. H., Optimization and Nonsmooth Analysis (Springer-Verlag, Berlin, 1998).Google Scholar
4. Fraigniaud, P., Ilcinkas, D., Peer, G., Pelc, A. and Peleg, D., “Graph exploration by a finite automaton,” Theoretical Comput. Sci. 345 (2–3), 331344 (2005).CrossRefGoogle Scholar
5. Lumelsky, V. J., Sensing, Intelligence, Motion: How Robots and Humans Move in an Unstructured World (Wiley-Interscience, Hoboken, New Jersey, 2005).CrossRefGoogle Scholar
6. Lumelsky, V. J. and Stepanov, A. A., “Path planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape,” Algorithmica 2, 403430 (1987).CrossRefGoogle Scholar
7. Kalman, R. E., “Contributions to the theory of optimal control,” Boletín de la Sociedad Matemática Mexicana 5, 102119 (1960).Google Scholar
8. Kalman, R. E., “A new approach to linear filtering and prediction problems,” J. Basic Eng. 82 (D), 3545 (1960).CrossRefGoogle Scholar
9. Kalman, R. E. and Bucy, R. S., “New results in linear filtering and prediction theory,” J. Basic Eng. 83 (1), 95108 (1961).CrossRefGoogle Scholar
10. Bennett, S., Chap. Process control: Technology and theory in A history of control engineering, 1930–1955. IET. 50–60, (1992).Google Scholar
11. Salzman, O. and Halperin, D., “Asymptotically near-optimal RRT for fast, high-quality motion planning,” IEEE Trans. Robot. 32 (3), 473483 (Jun. 2016).CrossRefGoogle Scholar
12. Latombe, J. C., Robot Motion Planning (Kluwer, New York, 1991).CrossRefGoogle Scholar
13. Sezer, V. and Gokasan, M., “A novel obstacle avoidance algorithm: Follow the gap method,” Robot. Autonomous Syst. 60 (9), 11231134 (2012).CrossRefGoogle Scholar
14. Alvarez and Sanchez, “Reactive navigation in real environments using partial center of area method,” Robot. Autonomous Syst. 58 (12), 12311237 (2010).CrossRefGoogle Scholar
15. Zhang, L., Kim, Y. J. and Manocha, D., “A Hybrid Approach for Complete Motion Planning,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2007) pp. 7–14.Google Scholar
16. Lu, Y., Huo, X., Arslan, O. and Tsiotras, P., “Incremental multi-scale search algorithm for dynamic path planning with low worst-case complexity,” IEEE Trans. Syst., Man Cybernetics B: Cybernetics 41 (6), 15561570 (2011).Google Scholar
17. Cowlagi, R. V. and Tsiotras, P., “Hierarchical motion planning with dynamical feasibility guarantees for mobile robotic vehicles,” IEEE Trans. Robot. 28 (2), 379395 (2012).CrossRefGoogle Scholar
18. Kala, R., Shukla, A. and Tiwari, R., “Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning,” Artif. Intell. Rev. 33 (4), 275306 (2010).CrossRefGoogle Scholar
19. Savkin, A. and Li, H., “A safe area search map building algorithm for a wheeled mobile robot in complex unknown cluttered environments,” Robotica 36 (1), 96118 (2018).CrossRefGoogle Scholar
20. Rone, W. and Ben-Tzvi, P., “Mapping, localization and motion planning in mobile multi-robotic systems,” Robotica 31 (1), 123 (2013). doi: 10.1017/S0263574712000021.CrossRefGoogle Scholar
21. Maddahi, Y., Sepehri, N., Maddahi, A. and Abdolmohammadi, M., “Calibration of wheeled mobile robots with differential drive mechanisms: An experimental approach,” Robotica 30 (6), 10291039 (2012).CrossRefGoogle Scholar
22. Hwang, Y. and Lee, J., “Robust 2D map building with motion-free ICP algorithm for mobile robot navigation,” Robotica 35 (9), 18451863 (2017).CrossRefGoogle Scholar
23. Kambhampati, S. and Davis, L., “Multi resolution path planning for mobile robots,” IEEE J. Robot. Autom. 2 (3), 135145 (1986).CrossRefGoogle Scholar
24. Noborio, H., Naniwa, T. and Arimoto, S., “A quadtree-based path-planning algorithm for a mobile robot,” J. Intell. Robot. Syst. 7 (4), 574576 (1990).Google Scholar
25. Kala, R., Shukla, A. and Tiwari, R., “Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness,” Neurocomputing 74 (14–15), 23142335 (2011).CrossRefGoogle Scholar
26. Negenborn, R., Robot Localization and Kalman Filters On finding your position in a noisy world Master's Thesis, (Utrecht University, Utrecht, Netherlands, 2003).Google Scholar

Paliwal and Kala supplementary material 1

Supplementary Video

Download Paliwal and Kala supplementary material 1(Video)
Video 92.7 MB

Paliwal and Kala supplementary material 2

Supplementary Video

Download Paliwal and Kala supplementary material 2(Video)
Video 6.8 MB