ANALYTICAL REVIEW OF NAVIGATION ISSUES OF MOBILE ROBOTS IN INDOOR ENVIRONMENT

DOI: 10.31673/2412-4338.2023.038087

Authors

  • Л. Д. Ганенко, (Hanenko L. D.) State University of Information and Communication Technologies, Kyiv Volodymyr Vynnychenko Central Ukrainian State University, Kropyvnytskyi
  • В. В. Жебка, (Zhebka V. V.) State University of Information and Communication Technologies, Kyiv

Abstract

Today, mobile robotics is one of the fastest growing areas of scientific research. Thanks to the wide range of capabilities of mobile robots, their use has been implemented in many areas: space exploration, construction, transportation, patrolling, emergency rescue operations, industrial automation, medicine, services, etc. The study of mobile robot navigation has rapidly progressed from robot movement along a fixed line to movement with environmental recognition and adaptation to dynamic objects, such as people surrounding the robots. The article deals with the problem of navigation of mobile robots in closed spaces. The interaction of such mobile robot systems as movement, perception, cognition, and navigation is described. The problems of movement are solved by understanding the mechanism and kinematics, dynamics, and control theory. Perception covers the area of sensor signal analysis. Cognitive functions are responsible for analyzing input data and performing appropriate actions by a mobile robot to achieve goals. Navigation requires knowledge of planning algorithms, information theory, and artificial intelligence to create real-time trajectories and avoid collisions with obstacles. To ensure coordinated operation, these systems must be united by a control unit. The paper analyzes the types of mobile robots and their architectural features. The main tasks of mobile robot navigation are considered: environment modeling, localization, path planning and obstacle avoidance. The article investigates algorithms and methods of localization of mobile robots, provides a classification of methods of path planning, and conducts a comparative analysis of them. The study identified areas for future research: increasing the battery life of mobile robots; improving systems for tracking the location of a mobile robot in space; ensuring optimal decision-making by a robot in real time; integrating new sensors and improving existing ones; improving the functional interaction of mobile robots with people.

Keywords: mobile robots, modeling, navigation, localization, path planning methods, decision support.

References:
1. Lee, Ho & Enriquez, John Laurence & Lee, Geon. (2022). Robotics 4.0: Challenges and Opportunities in the 4th Industrial Revolution.
2. Anbalagan Loganathan, Nur Syazreen Ahmad. (2023) A systematic review on recent advances in autonomous mobile robot navigation, Engineering Science and Technology, an International Journal, 40, 101343.
3. S. Gao, R. Song and Y. Li, (2018) Cooperative Control of Multiple Nonholonomic Robots for Escorting and Patrolling Mission Based on Vector Field, in IEEE Access, vol. 6, p. 41883-41891.
4. K. C. Okafor and O. M. Longe, (2022) WearROBOT: An Energy Conservative Wearable Obstacle Detection Robot With LP Multi-Commodity Graph, in IEEE Access, vol. 10, p. 105843-105865.
5. B. P. E. Alvarado Vasquez, R. Gonzalez, F. Matia and P. De La Puente, (2018) Sensor Fusion for Tour-Guide Robot Localization, in IEEE Access, vol. 6, p. 78947-78964.
6. R. Abiyev, D. Ibrahim, B. Erin, (2010) Navigation of mobile robots in the presence of obstacles, Advances in Engineering Software, vol. 41, p. 1179-1186.
7. Park, Jaesung. (2020) Real-time Robot Motion Planning Algorithms and Applications Under Uncertainty.
8. D. Jin, Z. Fang and J. Zeng, (2020) A Robust Autonomous Following Method for Mobile Robots in Dynamic Environments, in IEEE Access, vol. 8, p. 150311-150325.
9. Mahajan, Hemant & Uke, Nilesh & Pise, Priya & Shahade, Makarand & Dixit, Vandana & Bhavsar, Swapna & Deshpande, Sarita. (2022). Automatic robot Manoeuvres detection using computer vision and deep learning techniques: a perspective of internet of robotics things (IoRT). Multimedia Tools and Applications.
10. H. -T. L. Chiang, A. Faust, M. Fiser and A. Francis, (2019) Learning Navigation Behaviors End-to-End With AutoRL, in IEEE Robotics and Automation Letters, vol. 4, no. 2, p. 2007-2014.
11. A. Antonucci, G. P. R. Papini, P. Bevilacqua, L. Palopoli and D. Fontanelli, (2022) Efficient Prediction of Human Motion for Real-Time Robotics Applications With Physics-Inspired Neural Networks, in IEEE Access, vol. 10, p. 144-157.
12. Gurjeet Singh, V.K. Banga. (2022). Robots and its types for industrial applications, Materials Today: Proceedings, vol. 60, p. 1779-1786.
13. Bekey G. (2005) Autonomous Robots — From Biological Inspiration To Implementation And Control. MIT Press, p. 577.
14. R. Siegwart, I. R. Nourbakhsh and D. Scaramuzza, (2011), Introduction to Autonomous Mobile Robots, Cambridge, MA, USA:MIT Press, p. 472.
15. Trojnacki, Maciej & Dąbek, Przemysław. (2019). Mechanical Properties of Modern Wheeled Mobile Robots. Journal of Automation, Mobile Robotics and Intelligent Systems, vol.13, p. 3-13.
16. Blanco, Jose Luis & González-Jiménez, Javier & Fernández-Madrigal, J.-A. (2007). Mobile robot ego-motion estimation by proprioceptive sensor fusion. International Symposium on Signal Processing and its Applications 2007 (ISSPA07), p.1- 4.
17. Gorostiza, Ernesto Martín, José Luis Lázaro Galilea, Franciso Javier Meca Meca, David Salido Monzú, Felipe Espinosa Zapata, and Luis Pallarés Puerto. (2011) Infrared Sensor System for Mobile-Robot Positioning in Intelligent Spaces. Sensors, no. 5, p. 5416-5438.
18. Aqel, M.O.A., Marhaban, M.H., Saripan, M.I. et al. (2016) Review of visual odometry: types, approaches, challenges, and applications. SpringerPlus 5, 1897.
19. M. Kam, Xiaoxun Zhu and P. Kalata, (1997) Sensor fusion for mobile robot navigation, in Proceedings of the IEEE, vol. 85, no. 1, p. 108-119.
20. Rubio F, Valero F, Llopis-Albert C. (2019) A review of mobile robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems.;vol.16(2).
21. H. Durrant-Whyte and T. Bailey, (2006) Simultaneous localization and mapping: part I, in IEEE Robotics & Automation Magazine, vol. 13, no. 2, p. 99-110.
22. R. Barber, J. Crespo, C. Gomez, A. C. Hernamdez and M. Galli. (2018) Mobile Robot Navigation in Indoor Environments: Geometric Topological and Semantic Navigation. Applications of Mobile Robots, p. 1-25.
23. M. B. Alatise and G. P. Hancke. (2020) A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods, in IEEE Access, vol. 8, p. 39830-39846.
24. Andreas Nüchter, Joachim Hertzberg. (2008) Towards semantic maps for mobile robots, Robotics and Autonomous Systems, vol. 56, Issue 11, p. 915-926.
25. M. Colledanchise, D. Malafronte and L. Natale. (2020) Act perceive and plan in belief space for robot localization. IEEE International Conference on Robotics and Automation (ICRA), p. 3763-3769.
26. Y. Chen, W. Chen, L. Zhu, Z. Su, X. Zhou, Y. Guan, et al. (2019) A study of sensor-fusion mechanism for mobile robot global localization, Robotica, vol. 37, no. 11, p. 1835-1849.
27. Q. Li, R. Li, K. Ji and W. Dai. (2015) Kalman Filter and Its Application. 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, China, p. 74-77.
28. D. Fox, W. Burgard and S. Thrun. (1999) Markov localization for mobile robots in dynamic environments. Journal of Articial Intelligence Research, vol. 11, p. 391-427.
29. F. Dellaert, D. Fox, W. Burgard and S. Thrun. (1999) Monte Carlo localization for mobile robots, Proc. IEEE Int. Conf. Robot. Autom., vol. 2, p. 1322-1328.
30. Gutmann, J-S., and Dieter Fox. (2002) An experimental comparison of localization methods continued. In IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, p. 454-459.
31. LI, Xiuyun & LIU, Fei & LIU, Juan & LIANG, Shan. (2017). Obstacle avoidance for mobile robot based on improved dynamic window approach. Turkish journal of electrical engineering & computer sciences. vol. 25. p. 666-676.
32. O. A. Abubakr, M. A. Jaradat and M. F. Abdel-Hafez. (2022) Intelligent Optimization of Adaptive Dynamic Window Approach for Mobile Robot Motion Control Using Fuzzy Logic, in IEEE Access, vol. 10, p. 119368-119378.
33. V. Dubey, B. Patel and S. Barde. (2023) Path Optimization and Obstacle Avoidance using Gradient Method with Potential Fields for Mobile Robot. 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, p. 1358-1364.
34. Zohaib, Mohammad & Pasha, Mustafa & Riaz, Raja Ali & Javaid, Nadeem & Ilahi, M. & Khan, Rahim. (2013). Control Strategies for Mobile Robot With Obstacle Avoidance. Journal of Basic and Applied Scientific Reseach, vol. 3. p. 1027-1036.
35. Kim, Pil & Park, Chang & Jong, Yun & Yun, Jea & Mo, Eun & Kim, Chin-Su & Jie, Min & Hwang, Soo & Lee, Kang. (2007). Obstacle Avoidance of a Mobile Robot Using Vision System and Ultrasonic Sensor. Journal of Institute of Control Robotics and Systems, vol. 15, p. 545-553.

Downloads

Published

2023-11-01

Issue

No. 3 (2023)

Section

Articles