Natural Navigation System Design for Indoor Mobile Robots

Yıl 2022, Cilt: 5 Sayı: 1, 73 - 83, 31.05.2022

Azeez Adebayo Hüseyin Metin Ertunç

https://doi.org/10.34088/kojose.1012914

Cited By: 1

Öz

Natural navigation simply refers to free navigation without the necessity of tapes, magnets, reflectors, or even wires. Many autonomous vehicles possess this as world maps are readily available and provide a perfect basis for machine learning solutions. However, this is not so much the case for indoor applications. Here, paths are often dynamic and more constrained; therefore, requiring the continuous identification, mapping and localization of the surrounding area. This work focuses on developing an indoor natural navigation system; the localization is achieved with a fusion of the wheel’s odometry to the on-board Inertial Measurement Unit (IMU i.e., a combination of relative localization and absolute localization) using Unscented Kalman Filter (UKF) as system’s encoder’s accumulation of errors is desired to be nullified while employing a PID control in correcting reference state errors. The map is simultaneously constructed using laws of geometry based on static points obtained from a Lidar, subsequently converted to an occupancy grid layout for effective path planning. In operation, tangency is applied in the avoidance of dynamic obstacles. The simulation results obtained in this study confirms the possibility of a simple, educational, indoor navigation system approach easily integrable by other mobile robots of the differential drive model.

Anahtar Kelimeler

Simultaneous Localization and Mapping, Indoor Natural Navigation, Sensor Fusion, PID, Differential Drive Robot

Kaynakça

• [1] Wei L., Cappelle C.,Ruichek Y., 2013. Camera/Laser/GPS Fusion Method for Vehicle Positioning Under Extended NIS-Based Sensor Validation. IEEE Transactions on Instrumentation and Measurement, 62(11), pp. 3110-3122. https://doi.org/10.1109/TIM.2013.2265476.
• [2] Bruno S., Oussama H., 2010. CoreSLAM: a SLAM Algorithm in less than 200 lines of C code. Mines ParisTech - Center of Robotics, Paris, FRANCE.
• [3] Bajracharya S., 2014. BreezySLAM: A Simple, efficient, cross-platform Python package for Simultaneous Localization and Mapping. Student Papers, Record Group 38, Special Collections and Archives, Leyburn Library, Washington and Lee University, Lexington, VA.
• [4] Tsardoulias E. G., Iliakopoulou A., Kargakos A. et al., 2016. A Review of Global Path Planning Methods for Occupancy Grid Maps Regardless of Obstacle Density. Journal of Intelligent & Robotic Systems, 84(1), p.p. 829–858. https://doi.org/10.1007/s10846-016-0362-z.
• [5] Zou Q., Sun Q., Chen L., Nie B., and Li Q., 2021. A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3063477.
• [6] Guran M., Fico T., Chovancova A., Duchon F., Hubinsky P., Dubravsky J., 2014. Localization of iRobot create using inertial measuring unit, 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), 2014, pp. 1-7, doi: 10.1109/RAAD.2014.7002261.
• [7] Suriya D., Srivenkata S., Sundarrajan G., Kiran S., Ragul B., Vidhya B., 2016. A Robust Approach for Improving the Accuracy of IMU based Indoor Mobile Robot Localization. ICINCO (2) 2016: 436-445. DOI:10.5220/0005986804360445.
• [8] Urrea C., Agramonte R., 2021. Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation. Journal of Sensors, 2021. https://doi.org/10.1155/2021/9674015.
• [9] Hart P. E.; Nilsson N. J.; Raphael B., 1968. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), pp.100–107. https://doi.org/10.1109/TSSC.1968.300136.
• [10] Purnama H. S., Sutikno T., Alavandar S. and Subrata A. C., 2019. Intelligent Control Strategies for Tuning PID of Speed Control of DC Motor - A Review. 2019 IEEE Conference on Energy Conversion (CENCON), pp.24-30. https://doi.org/10.1109/CENCON47160.2019.8974782.
• [11] Berntorp K., Årzén K. E., & Robertsson A., 2011. Sensor Fusion for Motion Estimation of Mobile Robots with Compensation for Out-of-Sequence Measurements. 11th International Conference on Control, Automation and Systems, pp. 211-216.
• [12] Janai J., Güney F., Behl A., Geiger A., 2020. Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art. Foundations and Trends® in Computer Graphics and Vision, 12(1–3), pp 1-308.
• [13] Khan M. S., Chowdhury S. S., Niloy N., Aurin F. T. Z., Ahmed T., 2018. Sonar-based SLAM Using Occupancy Grid Mapping and Dead Reckoning. TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 1707-1712. doi: 10.1109/TENCON.2018.8650124.
• [14] Mu X., He B., Zhang X., Song Y., Shen Y., Feng C., 2019. End-to-end navigation for Autonomous Underwater Vehicle with Hybrid Recurrent Neural Networks. Ocean Engineering, 194. ISSN 0029-8018, https://doi.org/10.1016/j.oceaneng.2019.106602.
• [15] Kang J. G., An S. Y., Kim S. and Oh S., 2009. Sonar based Simultaneous Localization and Mapping using a Neuro Evolutionary Optimization. 2009 International Joint Conference on Neural Networks, pp. 1516-1523, https://doi.org/10.1109/IJCNN.2009.5178826.
• [16] Wang J., Liu J., Kato N., 2019. Networking and Communications in Autonomous Driving: A Survey. IEEE Communications Surveys & Tutorials, 21(2), pp. 1243-1274. doi: 10.1109/COMST.2018.2888904.
• [17] Chiu C.C., Hsu J.C., Leu J.S., 2016. Implementation and analysis of Hybrid Wireless Indoor Positioning with iBeacon and Wi-Fi. 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 80-84. doi: 10.1109/ICUMT.2016.7765336.
• [18] Li J. et al., 2018. PSOTrack: A RFID-Based System for Random Moving Objects Tracking in Unconstrained Indoor Environment. IEEE Internet of Things Journal, 5(6), pp. 4632-4641. doi: 10.1109/JIOT.2018.2795893.
• [19] Lin P.T., Liao C.A. and Liang S.H., 2021. Probabilistic Indoor Positioning and Navigation (PIPN) of Autonomous Ground Vehicle (AGV) Based on Wireless Measurements. IEEE Access, 9, pp. 25200-25207. https://doi.org/10.1109/ACCESS.2021.3057415.
• [20] Jensfelt P., 2001. Approaches to Mobile Robot Localization in Indoor Environments. (Doctoral thesis, Royal Institute of Technology (KTH), Stockholm, Sweden). Retrieved from http://www.diva-portal.org/smash/get/diva2:8964/FULLTEXT01.pdf
• [21] Przemysławm P., Piotr K., 2020. Unscented Kalman filter application in personal navigation. Proc. SPIE 11442, Radioelectronic Systems Conference 2019, 114421C. DOI:10.1117/12.2564984
• [22] Si L., Yu J., Wu W., Ma J., Wu Q., Li S., 2017. RMHC-MR: Instance selection by random mutation hill climbing algorithm with MapReduce in big data. Procedia Computer Science, 111, pp.252-259. https://doi.org/10.1016/J.PROCS.2017.06.061.