Core Technologies and Applications of Robotic Navigation Systems
Research Article
Open Access
CC BY

Core Technologies and Applications of Robotic Navigation Systems

Yuyang Lin 1*
1 University of Nottingham
*Corresponding author: ssyyl80@nottingham.edu.cn
Published on 11 November 2025
Volume Cover
ACE Vol.204
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-517-2
ISBN (Online): 978-1-80590-518-9
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Abstract

In recent years, robotics navigation has become a core component of autonomous navigation, enabling robots to sense their environments, localise themselves, and plan safe and efficient paths autonomously. This paper explores the core and fundamental technologies, including LiDAR, computer vision, infrared sensors, Simultaneous Localisation and Mapping (SLAM), GPS with RTK, path planning algorithms, vehicle-to-everything communication, and Direct Memory Access (DMA). It also introduces some typical applications of robotics navigation and its core technologies. Despite the widespread use of robotics navigation and its significant improvement, numerous challenges and difficulties are faced. These include the difficulty of navigation, especially in unknown or dynamic environments. The heavy load of dealing with multiple data streams from sensors is also a problem that should be overcome. Moreover, the social and ethical considerations, such as safety and trust in a human-shared area, remain unsolved. The future development of robotics navigation will focus on Artificial Intelligence combined with 6G communication and edge-cloud computing. Establishing ethical standards will also be essential for the future development of robotics navigation.

Keywords:

Robotics Navigation, Core Technologies, Autonomous Systems, Path Planning Algorithms, Multi-Sensor Fusion

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Lin,Y. (2025). Core Technologies and Applications of Robotic Navigation Systems. Applied and Computational Engineering,204,46-53.

References

[1]. Abdalla, H., Doukhi, O., Hong, D., & Lee, D. (2024). Purpose-based delivery robot hybrid control system design using PixHawk and edge computing. Journal of Institute of Control Robotics and Systems, 30(5), 531–539.

[2]. Amelung, J., & Galle, C. (2019). Solid-State LiDAR: Umgebungssensorik für sicheres autonomes Fahren. In MikroSystemTechnik Kongress 2019.

[3]. Chen, C., Pei, L., Xu, C., Zou, D., Qi, Y., Zhu, Y., & Li, T. (2019). Trajectory optimization of LiDAR SLAM based on local pose graph. In Lecture Notes in Electrical Engineering (Vol. 562, pp. 360–370). Springer.

[4]. Corke, P. (2011). Navigation. In Springer Tracts in Advanced Robotics (pp. 87–106). Springer.

[5]. Chi, Y., Meng, X., Xiao, S., Lu, X., & Wu, S. (2025). Multi-sensor fusion SLAM algorithm based on line feature optical flow. Acta Optica Sinica, 45(8), 0815002.

[6]. Choudhary, A., Chavda, J. B., Ganatra, A. P., & Nayak, R. J. (2016). Performance evaluation PL330 DMA controller for bulk data transfer in Zynq SoC. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (Vol. 13, pp. 1811–1815).

[7]. Ciocoiu, T. I., & Moldoveanu, F. D. (2017). Vision-based localisation, stereo rig calibration, and rectification. In 2017 International Conference on Optimization of Electrical and Electronic Equipment (pp. 1013–1018).

[8]. De Freitas, R. S., Romero-Garcés, A., Marfil, R., Vicente-Chicote, C., Martínez-Cruz, J., Inglés-Romero, J. F., & Bandera, A. (2020). QOS Metrics-in-the-Loop for better robot Navigation. In Advances in Intelligent Systems and Computing (pp. 94–108).

[9]. Detesan, O., & Moholea, I. F. (2024). Unimate and beyond: Exploring the genesis of industrial robotics. In Mechanisms and Machine Science (pp. 263–271).

[10]. Du, Y., Huang, G., Zhang, Q., Gao, Y., & Gao, Y. (2019). A new asynchronous RTK method to mitigate base station observation outages. Sensors, 19(15), 3376.

[11]. Feng, J., Wan, H., Yan, H., Li, J., & Fu, J. (2025). The path planning of a mobile robot based on the improved A-star algorithm combined with DWA. In 2025 4th International Symposium on Computer Applications and Information Technology (pp. 1241–1244).

[12]. Gantenbein, D. (2022). How Amazon scientists are helping the Scout delivery device find a path to success. Amazon Science.

[13]. Gavrilut, I., Tiponut, V., Gacsadi, A., & Tepelea, L. (2008). Obstacles avoidance method for an autonomous mobile robot using two IR sensors. Journal of Electrical and Electronics Engineering, 1(1), 194–197.

[14]. Geng, J., Mokhtarzadeh, A. A., Fei, H., Gao, H., Ding, N., Kong, R., & Zhou, J. (2023). Robot positioning and navigation technology based on integration of the Global Navigation Satellite System and real-time kinematics. Journal of Physics: Conference Series, 2467(1), 012027.

[15]. Gupta, R. A., Agarwal, A. K., Chow, M., & Wang, W. (2007). Performance assessment of data and time-sensitive wireless distributed networked-control-systems in presence of information security. In MILCOM 2007 Proceedings (pp. 1–7).

[16]. Herlambang, T., Indriawati, K., Akbar, R. M., & Nurhadi, H. (2024). PID path following control system design on unmanned autonomous forklift prototype. Barekeng Jurnal Ilmu Matematika dan Terapan, 18(4), 2093–2112.

[17]. Hodas, S., Izvoltova, J., & Rekus, D. (2021). Trends in inertial navigation technologies. IOP Conference Series: Earth and Environmental Science, 906(1), 012069.

[18]. Huang, N. G., Lin, N. H., Lin, N. K., & Kao, N. S. (2010). Intelligent auto-saving energy robotic lawn mower. In 2010 IEEE International Conference on Systems, Man and Cybernetics (pp. 4130–4136).

[19]. Ju, W. (2021). Application of autonomous navigation in robotics. Journal of Physics: Conference Series, 1906(1), 012018.

[20]. Karur, K., Sharma, N., Dharmatti, C., & Siegel, J. E. (2021). A survey of path planning algorithms for mobile robots. Vehicles, 3(3), 448–468.

References

[1]. Abdalla, H., Doukhi, O., Hong, D., & Lee, D. (2024). Purpose-based delivery robot hybrid control system design using PixHawk and edge computing. Journal of Institute of Control Robotics and Systems, 30(5), 531–539.

[2]. Amelung, J., & Galle, C. (2019). Solid-State LiDAR: Umgebungssensorik für sicheres autonomes Fahren. In MikroSystemTechnik Kongress 2019.

[3]. Chen, C., Pei, L., Xu, C., Zou, D., Qi, Y., Zhu, Y., & Li, T. (2019). Trajectory optimization of LiDAR SLAM based on local pose graph. In Lecture Notes in Electrical Engineering (Vol. 562, pp. 360–370). Springer.

[4]. Corke, P. (2011). Navigation. In Springer Tracts in Advanced Robotics (pp. 87–106). Springer.

[5]. Chi, Y., Meng, X., Xiao, S., Lu, X., & Wu, S. (2025). Multi-sensor fusion SLAM algorithm based on line feature optical flow. Acta Optica Sinica, 45(8), 0815002.

[6]. Choudhary, A., Chavda, J. B., Ganatra, A. P., & Nayak, R. J. (2016). Performance evaluation PL330 DMA controller for bulk data transfer in Zynq SoC. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (Vol. 13, pp. 1811–1815).

[7]. Ciocoiu, T. I., & Moldoveanu, F. D. (2017). Vision-based localisation, stereo rig calibration, and rectification. In 2017 International Conference on Optimization of Electrical and Electronic Equipment (pp. 1013–1018).

[8]. De Freitas, R. S., Romero-Garcés, A., Marfil, R., Vicente-Chicote, C., Martínez-Cruz, J., Inglés-Romero, J. F., & Bandera, A. (2020). QOS Metrics-in-the-Loop for better robot Navigation. In Advances in Intelligent Systems and Computing (pp. 94–108).

[9]. Detesan, O., & Moholea, I. F. (2024). Unimate and beyond: Exploring the genesis of industrial robotics. In Mechanisms and Machine Science (pp. 263–271).

[10]. Du, Y., Huang, G., Zhang, Q., Gao, Y., & Gao, Y. (2019). A new asynchronous RTK method to mitigate base station observation outages. Sensors, 19(15), 3376.

[11]. Feng, J., Wan, H., Yan, H., Li, J., & Fu, J. (2025). The path planning of a mobile robot based on the improved A-star algorithm combined with DWA. In 2025 4th International Symposium on Computer Applications and Information Technology (pp. 1241–1244).

[12]. Gantenbein, D. (2022). How Amazon scientists are helping the Scout delivery device find a path to success. Amazon Science.

[13]. Gavrilut, I., Tiponut, V., Gacsadi, A., & Tepelea, L. (2008). Obstacles avoidance method for an autonomous mobile robot using two IR sensors. Journal of Electrical and Electronics Engineering, 1(1), 194–197.

[14]. Geng, J., Mokhtarzadeh, A. A., Fei, H., Gao, H., Ding, N., Kong, R., & Zhou, J. (2023). Robot positioning and navigation technology based on integration of the Global Navigation Satellite System and real-time kinematics. Journal of Physics: Conference Series, 2467(1), 012027.

[15]. Gupta, R. A., Agarwal, A. K., Chow, M., & Wang, W. (2007). Performance assessment of data and time-sensitive wireless distributed networked-control-systems in presence of information security. In MILCOM 2007 Proceedings (pp. 1–7).

[16]. Herlambang, T., Indriawati, K., Akbar, R. M., & Nurhadi, H. (2024). PID path following control system design on unmanned autonomous forklift prototype. Barekeng Jurnal Ilmu Matematika dan Terapan, 18(4), 2093–2112.

[17]. Hodas, S., Izvoltova, J., & Rekus, D. (2021). Trends in inertial navigation technologies. IOP Conference Series: Earth and Environmental Science, 906(1), 012069.

[18]. Huang, N. G., Lin, N. H., Lin, N. K., & Kao, N. S. (2010). Intelligent auto-saving energy robotic lawn mower. In 2010 IEEE International Conference on Systems, Man and Cybernetics (pp. 4130–4136).

[19]. Ju, W. (2021). Application of autonomous navigation in robotics. Journal of Physics: Conference Series, 1906(1), 012018.

[20]. Karur, K., Sharma, N., Dharmatti, C., & Siegel, J. E. (2021). A survey of path planning algorithms for mobile robots. Vehicles, 3(3), 448–468.

Cite this article

Lin,Y. (2025). Core Technologies and Applications of Robotic Navigation Systems. Applied and Computational Engineering,204,46-53.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-517-2(Print) / 978-1-80590-518-9(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.204
ISSN: 2755-2721(Print) / 2755-273X(Online)

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