Mapping Cheap Robots with Noisy IR Sensors

Abstract

The advancements in robotics has given rise to the manufacturing of affordable educational mobile robots. Due to their size and cost, they possess limited global localization and mapping capability. The purpose of producing these robots is not fully materialized if advance algorithms cannot be demonstrated on them. In this paper, we address this limitation by just using dead-reckoning and low bandwidth noisy infrared sensors for localization in an unknown environment. We demonstrate Extended Kalman Filter implementation, produce a map of the unknown environment by Occupancy grid mapping and based on this map, perform particle filtering to do Monte-Carlo Localization. In our implementation, we use the low cost e-puck mobile robot, which performs these tasks. We also putforth an empirical evaluation of the results, which shows convergence. The presented results provide a base to further build on the navigation and path-planning problems.

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References

1. A. Prorok, A. B. and Martinoli, A. (2012). Low-cost collaborative localization for large-scale multi-robot systems. In Robotics and Automation (ICRA), 2012 IEEE International Conference on. IEEE.
2. A.D. Cucci, M. Migliavacca, A. B. and Matteucci, M. (2016). Development of mobile robots using offthe-shelf open-source hardware and software components for motion and pose tracking. In Intelligent Autonomous Systems. SPRINGER.
3. Ahmad, H. and Namerikawa, T. (2010). Robot localization and mapping problem with unknown noise characteristics. In 2010 IEEE International Conference on Control Applications. IEEE.
4. C. Sprunk, J. R öwekämper, G. P. L. S. G. T. W. B. and Jalobeanu, M. (2016). An experimental protocol for benchmarking robotic indoor navigation. In Experimental Robotics. SPRINGER.
5. Colleens, T. and Colleens, J. (2007). Occupancy grid mapping: An empirical evaluation. In Mediterranean Conference on Control & Automation. IEEE.
6. D. Kortenkamp, R. B. and Murphy, R. (1998). Ai-based mobile robots: Case studies of successful robot systems. CAMBRIDGE.
7. Elfes, A. (1989). Occupancy grids: A probabilistic framework for robot perception and navigation. CMU.
8. F. Mondada, M. Bonani, X. R. J. P. C. C. A. K. S. M. J. Z. D. F. and Martinoli, A. (2009). The e-puck, a robot designed for education in engineering. In conference on autonomous robot systems and competitions. IPCB.
9. J. McLurkin, A. Lynch, J. A. S. R. T. B. A. C. K. F. and Bilstein, S. (2013). A low-cost multi-robot system for research, teaching, and outreach. In Distributed Autonomous Robotic Systems. SPRINGER.
10. M. Markom, A. Adom, E. T. S. S. A. R. and Shakaff, M. (2015). A mapping mobile robot using rp lidar scanner. In International Symposium on Robotics and Intelligent Sensors. IEEE.
11. P. Schmuck, S. S. and Zell, A. (2016). Hybrid metrictopological 3d occupancy grid maps for large-scale mapping. In IFAC-PapersOnLine. ELSEVIER.
12. S. Bazeille, E. B. and Filliat, D. (2015). A light visual mapping and navigation framework for low-cost robots. In Journal of Intelligent Systems. DE GRUYTER.
13. S. Thrun, W. B. and Fox, D. (2005). Probabilistic Robotics. MIT Press, USA, 2nd edition.
14. S. Wang, F.Colas, M. L. F. M. and Magnenat, S. (2016). Localization of inexpensive robots with low-bandwidth sensors. In 13th International Symposium on Distributed Autonomous Robotic Systems. INRIA.
15. Shim, J. and Cho, Y. (2016). A mobile robot localization via indoor fixed remote surveillance cameras. InSensors. MDPI.
16. Tribelhorn, B. and Dodds, Z. (2007). Evaluating the roomba: A low-cost, ubiquitous platform for robotics research and education. In International Conference on Robotics and Automation. IEEE.