Application of an artificial neural network for planning the trajectory of a mobile robot

• Autorzy: Białek Marcin , Nowak Patryk , Rybarczyk Dominik

Identyfikatory

DOI

10.14313/JAMRIS/1-2020/2

• Języki publikacji: EN

Abstrakty

EN

This paper presents application of a neural network in the task of planning a mobile robot trajectory. First part contains a review of literature focused on the mobile robots’ orientation and overview of artificial neural networks’ application in area of robotics. In these sections devices and approaches for collecting data of mobile robots environment have been specified. In addition, the principle of operation and use of artificial neural networks in trajectory planning tasks was also presented. The second part focuses on the mobile robot that was designed in a 3D environment and printed with PLA material. The main onboard logical unit is Arduino Mega. Control system consist of 8-bits microcontrollers and 13 Mpix camera. Discussion in part three describes the system positioning capability using data from the accelerometer and magnetometer with overview of data filtration and the study of the artificial neural network implementation to recognize given trajectories. The last chapter contains a summary with conclusions.

Słowa kluczowe

EN

artificial neural network; mobile robot; machine vision

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 1
• Strony: 13--23
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Białek Marcin

• Department of Mechatronic Devices, Poznan University of Technology, Poznan, Poland

autor

Nowak Patryk

• Department of Mechatronic Devices, Poznan University of Technology, Poznan, Poland

autor

Rybarczyk Dominik

• Department of Mechatronic Devices, Poznan University of Technology, Poznan, Poland

Bibliografia

• [1] M. Garbacz, “Planowanie ścieżki dla robota mobilnego na podstawie informacji z czujników odległościowych”, Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie, vol. 10, no. 3, 2006, 135–141.
• [2] K. Bhagat, S. Deshmukh, S. Dhonde, S. Ghag and V. Waghmare, “Obstacle Avoidance Robot”, International Journal of Science, Engineering and Technology Research, vol. 5, no. 2, 2016, 439–442.
• [3] C. Randell and H. Muller, “Low Cost Indoor Positioning System”. In: G. D. Abowd, B. Brumitt and S. Shafer (eds.), Ubicomp 2001: Ubiquitous Computing, 2001, 42–48, DOI: 10.1007/3-540-45427-6_5.
• [4] Z. Tan, S. Bi, H. Wang and Z. Wang, “Target Tracking Control of Mobile Robot Based on Ultrasonic Sensor”. In: Proceedings of the 6th Intertional Conference on Information Engineering for Mechanics and Materials, 2016, 60–64,DOI: 10.2991/icimm-16.2016.13.
• [5] S. Adarsh, S. M. Kaleemuddin, D. Bose and K. I. Ramachandran, “Performance comparison of Infrared and Ultrasonic sensors for obstacles of different materials in vehicle/ robot navigation applications”, IOP Conference Series: Materials Science and Engineering, vol. 149, 2016, DOI: 10.1088/1757-899X/149/1/012141.
• [6] J. M. Soares, I. Navarro and A. Martinoli, “The Khepera IV Mobile Robot: Performance Evaluation, Sensory Data and Software Toolbox”. In: L. P. Reis, A. P. Moreira, P. U. Lima, L. Montano and V. Muñoz-Martinez (eds.), Robot 2015: Second Iberian Robotics Conference, vol. 417, 2016, 767–781,DOI: 10.1007/978-3-319-27146-0_59.
• [7] M. Januszka, M. Adamczyk and W. Moczulski, “Nieholonomiczny autonomiczny robot mobilny do inspekcji obiektów technicznych”, Prace Naukowe Politechniki Warszawskiej. Elektronika, vol. 166, no. 1, 2008, 143–152.
• [8] J. B.-Y. Tsui, Fundamentals of Global Positioning System Receivers: A Software Approach, John Wiley & Sons, Inc., 2004.
• [9] A. Wa̧ sik, R. Ventura, J. N. Pereira, P. U. Lima and A. Martinoli, “Lidar-Based Relative Position Estimation and Tracking for Multi-robot Systems”. In: L. P. Reis, A. P. Moreira, P. U. Lima, L. Montano and V. Muñoz-Martinez (eds.), Robot 2015: Second Iberian Robotics Conference, vol. 417, 2016, 03–16, DOI: 10.1007/978-3-319-27146-0_1.
• [10] Z. Huang, J. Zhu, L. Yang, B. Xue, J. Wu and Z. Zhao, “Accurate 3-D Position and Orientation Method for Indoor Mobile Robot Navigation Based on Photoelectric Scanning”, IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 9, 2015, 2518–2529, DOI: 10.1109/TIM.2015.2415031.
• [11] T. Więk, “Laserowy system nawigacji platformy mobilnej na przykładzie skanera NAV300”, Pomiary Automatyka Robotyka, vol. 15, no. 2, 2011, 374–381.
• [12] F. B. P. Malavazi, R. Guyonneau, J.-B. Fasquel, S. Lagrange and F. Mercier, “LiDAR-only based navigation algorithm for an autonomous agricultural robot”, Computers and Electronics in Agriculture, vol. 154, 2018, 71–79, DOI: 10.1016/j.compag.2018.08.034.
• [13] B. Siemiątkowska, “Hybrydowa reprezentacja otoczenia robota mobilnego”, Pomiary Automatyka Robotyka, vol. 11, no. 2, 2007.
• [14] D. S. O. Correa, D. F. Sciotti, M. G. Prado, D. O. Sales, D. F. Wolf and F. S. Osorio, “Mobile Robots Navigation in Indoor Environments Using Kinect Sensor”. In: 2012 Second Brazilian Conference on Critical Embedded Systems, 2012, 36–41, DOI: 10.1109/CBSEC.2012.18.
• [15] P. Fankhauser, M. Bloesch, D. Rodriguez, R. Kaestner, M. Hutter and R. Siegwart, “Kinect 2 for mobile robot navigation: Evaluation and modeling”. In: 2015 International Conference on Advanced Robotics (ICAR), 2015, 388–394, DOI: 10.1109/ICAR.2015.7251485.
• [16] A. Oliver, S. Kang, B. C. Wünsche and B. MacDonald, “Using the Kinect as a navigation sensor for mobile robotics”. In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 509–514, DOI: 10.1145/2425836.2425932.
• [17] T. Pire, T. Fischer, J. Civera, P. De Cristoforis and J. J. Berlles, “Stereo parallel tracking and mapping for robot localization”. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, 1373–1378, DOI: 10.1109/IROS.2015.7353546.
• [18] K. Kaemarungsi and P. Krishnamurthy, “Modeling of indoor positioning systems based on location fingerprinting”. In: IEEE INFOCOM 2004, ol. 2, 2004, 1012–1022, DOI: 10.1109/INFCOM.2004.1356988.
• [19] C. Feng, W. S. A. Au, S. Valaee and Z. Tan, “Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing”, IEEE Transactions on Mobile Computing, vol. 11, no. 12, 2012, 1983–1993, DOI: 10.1109/TMC.2011.216.
• [20] Ł. Błaszczyk, “Podstawy Teorii Oszczędnego Próbkowania (Compressed Sensing – Theoretical Preliminaries)”, B.S. thesis, Faculty of Mathematics and Information Science, Warsaw University of Technology, 2014 (in Polish).
• [21] W. Kang, S. Nam, Y. Han and S. Lee, “Improved heading estimation for smartphone-based indoor positioning systems”. In: 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications – (PIMRC), 2012, 2449–2453, DOI: 10.1109/PIMRC.2012.6362768.
• [22] B. Muset and S. Emerich, “Distance Measuring using Accelerometer and Gyroscope Sensors”, Carpathian Journal of Electronic and Computer Engineering, vol. 5, no. 1, 2012, 83–86.
• [23] M. E. Qazizada and E. Pivarčiová, “Mobile Robot Controlling Possibilities of Inertial Navigation System”, Procedia Engineering, vol. 149, 2016, 404–413, DOI: 10.1016/j.proeng.2016.06.685.
• [24] Y. Pei and L. Kleeman, “Mobile robot floor classification using motor current and accelerometer measurements”. In: 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC), 2016, 545–552, DOI: 10.1109/AMC.2016.7496407.
• [25] K. S. Younis and A. A. Alkhateeb, “A New Implementation of Deep Neural Networks for Optical Character Recognition and Face Recognition”. In: Proceedings of the new trends in information technology, 2017, 157–162.
• [26] D. C. Cireşan, U. Meier, L. M. Gambardella and J. Schmidhuber, “Deep Big Multilayer Perceptrons for Digit Recognition”. In: G. Montavon, G. B. Orr and K.-R. Müller (eds.), Neural Networks: Tricks of the Trade, vol. 7700, 2012, 581–598, DOI: 10.1007/978-3-642-35289-8_31.
• [27] M. Dobrowolski, M. Dobrowolski and P. Kopniak, “Analiza możliwości wykorzystania czujników urządzeń mobilnych pod kontrolą zmodyfikowanych systemów operacyjnych (Analysis of the use of sensors in mobile devices with modified operating systems)”, Journal of Computer Sciences Institute, vol. 5, 2017, 193-199 (in Polish).
• [28] K. Seifert and O. Camacho, Implementing Positioning Algorithms Using Accelerometers, Application Note AN3397, Freescale Semiconductor, 2007.
• [29] “jarzebski/Arduino-HMC5883L: HMC5883L Triple Axis Digital Compass Arduino Library”.https://github.com/jarzebski/Arduino-HMC 5883L. Accessed on: 2020-05-28.
• [30] https://petrospsyllos.com/images/ssn-kurs-2/Obraz5.png. Accessed on: 17.06.2020.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).