3D Absolute pose estimation of a staircase cleaning robot utilizing staircase geometry and minimal low-cost sensors

Thanks to its shape-shifting abilities, the sTetris robot can climb stairs while performing the cleaning task. The sTetris robot’s overall system operation depends on localization and positioning data, which are essential for its goal of autonomously navigating multi-floor environments. The mobile robots designed to work indoors generally rely on external systems for localization information. Regretfully, this frequently requires additional hardware fixing or changes to the indoor working environment in order to achieve accurate three-dimensional (3D) position and orientation (pose) for successful operation of the mobile platform. Nonetheless, the robot can be localized on the staircase by utilizing the information of the staircase’s geometry measurements, which are known ahead of time. This article demonstrates how the known geometry of staircases and measurements from minimal number of sensors can be used to accomplish 3D pose of the robot. Experiments carried out on a real robot in an authentic indoors setting successfully demonstrate the effectiveness of the suggested approach.

1. Ilyas M., Cho K., Baeg S.H., Park S. (2015) Drift reduction in IMU-only pedestrian navigation system in unstructured environment. 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015.

2. George M.B.K.W. (1988) A Navigation Control System for an Autonomous Mobile Robot. Robotics and Factories of the Future ’87. Springer, pp. 678–686.

3. Da Mota F.A.X., Rocha M.X., Rodrigues J.J.P.C., De Albuquerque V.H.C., De Alexandria A.R. (2018) Localization and navigation for autonomous mobile robots using petri nets in indoor environments. IEEE Access, 6, 31665–31676.

4. Burgard W., Fox D., Hennig D. (1996) Estimating the absolute position of a mobile robot using position probability grids. Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 1–15.

5. Goel P., Roumeliotis S.I., Sukhatme G.S. (1999) Robust localization using relative and absolute position estimates. 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 1134–1140.

6. Gorostiza E.M., Galilea J.L.L., Meca F.J.M., Monzú D.S., Zapata F.E., Puerto L.P. (2011) Infrared sensor system for mobile-robot positioning in intelligent spaces. Sensors, 11, 5416–5438.

7. Borenstein J., Everett H.R., Feng L., Wehe D. (1997) Mobile robot positioning: Sensors and techniques. Journal of Robotics and Systems, 14, 231–249.

8. Alatise M.B., Hancke G.P. (2017) Pose estimation of a mobile robot based on fusion of IMU data and vision data using an extended Kalman filter. Sensors (Switzerland), 17.

9. Cho B.S., Moon W., Seo W.J., Baek K.R. (2011) A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding. Journal of Mechanical Science and Technology, no. 25, 2907–2917.

10. North E., Georgy J., Tarbouchi M., Iqbal U., Noureldin A. (2009) Enhanced mobile robot outdoor localization using INS/GPS integration. Proceedings of the 2009 International Conference on Computer Engineering and Systems, pp. 127–132.

11. Hammouche M., Sakhi S., Belhocine M., Elourdi A., Bouaziz S. (2016) A Fuzzy Controller for GPS/ INS/Odom Integrated Navigation System. Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics, pp. 390–397.

12. Biswas J., Veloso M. (2010) WiFi localization and navigation for autonomous indoor mobile robots. 2010 IEEE International Conference on Robotics and Automation, pp. 4379–4384.

13. Lim J., Lee S., Tewolde G., Kwon J. (2015) Indoor localization and navigation for a mobile robot equipped with rotating ultrasonic sensors using a smartphone as the robot’s brain. IEEE International Conference on Electro Information Technology, vol. 2015–June, pp. 621–625.

14. Xu D., Han L., Tan M., Li Y.F. Ceiling-based visual positioning for an indoor mobile robot with monocular vision. IEEE Transactions on Industrial Electronics 2009, 56, 1617–1628.

15. Lee T.J., Kim C.H., Cho D.I.D. A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots. IEEE Transactions on Industrial Electronics 2019, 66, pp. 318–328.

16. Park S., Hashimoto S. Autonomous navigation system for mobile robot using randomly distributed passive RFID tags. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2010, E93–A, pp. 711–719.

17. Falsi C., Dardari D., Mucchi L., Win M.Z. (2006) Time of arrival estimation for UWB localizers in realistic environments. EURASIP Journal on Applied Signal Processing 2006, pp. 1–13.

18. Tedeschi A., Calcaterra S., Benedetto F. Ultrasonic Radar system (URAS): Arduino and virtual reality for a light-free mapping of indoor environments. IEEE Sensors Journal 2017, 17, 4595–4604.

19. Ilyas M., Yuyao S., Mohan R.E., Devarassu M., Kalimuthu M. (2018) Design of sTetro: A Modular, Reconfigurable, and Autonomous Staircase Cleaning Robot.

20. Tetris game. https://en.wikipedia.org/ (Accessed on 2 Feb. 2023).

21. Hayat A.A., Yi L., Kalimuthu M., Elara M., Wood K.L. (2022) Reconfigurable robotic system design with application to cleaning and maintenance. Journal of Mechanical Design, 144, 063305.