Open Access
| Issue |
MATEC Web Conf.
Volume 417, 2025
2025 RAPDASA-RobMech-PRASA-AMI Conference: Bridging the Gap between Industry & Academia - The 26th Annual International RAPDASA Conference, joined by RobMech, PRASA and AMI, co-hosted by CSIR and Tshwane University of Technology, Pretoria
|
|
|---|---|---|
| Article Number | 04010 | |
| Number of page(s) | 14 | |
| Section | Robotics and Mechatronics | |
| DOI | https://doi.org/10.1051/matecconf/202541704010 | |
| Published online | 25 November 2025 | |
- M. Law, H.S. Ahn, E. Broadbent, K. Peri, N. Kerse, E. Topou, B. MacDonald, Case studies on the usability, acceptability and functionality of autonomous mobile delivery robots in real-world healthcare settings. Intell. Serv. Robot. 14, 387–398 (2021). https://doi.org/10.1007/s11370-021-00364-8 [Google Scholar]
- A.A. Neloy, R.A. Bindu, S. Alam, R. Haque, M.S.A. Khan, N.M. Mishu, S. Siddique, Alpha-N-V2: Shortest path finder automated delivery robot with obstacle detection and avoiding system. Vietnam J. Comput. Sci. 7, 373–389 (2020). https://doi.org/10.1142/S2196888820500187 [Google Scholar]
- J.P.C. de Souza, C.M. Costa, L.F. Rocha, R. Arrais, A.P. Moreira, E.S. Pires, J. Boaventura-Cunha, Reconfigurable grasp planning pipeline with grasp synthesis and selection applied to picking operations in aerospace factories. Robot. Comput.-Integr. Manuf. 67, 102032 (2021). https://doi.org/10.1016/j.rcim.2020.102032 [Google Scholar]
- H. Taheri, S.R. Hosseini, M.A. Nekoui, Deep reinforcement learning with enhanced PPO for safe mobile robot navigation. arXiv preprint arXiv:2405.16266 (2024). https://doi.org/10.48550/arXiv.2405.16266 [Google Scholar]
- L. Tai, M. Liu, A robot exploration strategy based on Q-learning network. In Proc. IEEE Int. Conf. Real-Time Comput. Robot. (RCAR), 57–62 (2016). https://doi.org/10.1109/RCAR.2016.7784000 [Google Scholar]
- M. Aljamal, S. Patel, A. Mahmood, Comprehensive review of robotics operating system-based reinforcement learning in robotics. Appl. Sci. 15, 1840 (2025). https://doi.org/10.3390/app15041840 [Google Scholar]
- B.A. Labinghisa, D.M. Lee, Indoor localization system using deep learning based scene recognition. Multimed. Tools Appl. 81, 28405–28429 (2022). https://doi.org/10.1007/s11042-022-12853-y [Google Scholar]
- P. Jiang, D. Ergu, F. Liu, Y. Cai, B. Ma, A review of YOLO algorithm developments. Procedia Comput. Sci. 199, 1066–1073 (2022). https://doi.org/10.1016/j.procs.2022.01.135 [Google Scholar]
- R. Varghese, M. Sambath, YOLOv8: A novel object detection algorithm with enhanced performance and robustness. In Proc. Int. Conf. Adv. Data Eng. Intell. Comput. Syst. (ADICS), 1–6 (2024). https://doi.org/10.1109/ADICS60510.2024.10599351 [Google Scholar]
- M. Safaldin, N. Zaghden, M. Mejdoub, An improved YOLOv8 to detect moving objects. IEEE Access (2024). https://doi.org/10.1109/ACCESS.2024.3351894 [Google Scholar]
- H.A. Bui, T.T. Mac, X.T. Nguyen, A human tracking system for the Rocker-Bogie mobile robot utilizing the YOLOv8 network. Vietnam J. Comput. Sci. 1–22 (2025). https://doi.org/10.1142/S2196888825500029 [Google Scholar]
- Y. Zhu, T. Zhong, Y. Wang, J. Kan, F. Dong, K. Chen, Mobile robot tracking method based on improved YOLOv8 pedestrian detection algorithm. In Proc. 2nd Int. Conf. Mach. Learn. Cloud Comput. Intell. Min. (MLCCIM), 454–463 (2023). https://doi.org/10.1109/MLCCIM58586.2023.10278561 [Google Scholar]
- I. Syafalni, A.W. Sinisuka, D.K.A. Tauhid, F. Ahmad, M.A.P. Yasa, S.A. Wen, T. Adiono, Exploration robot based on YOLOv8 algorithm. In Proc. Asia Pac. Signal Inf. Process. Assoc. Annu. Summit Conf. (APSIPA ASC), 1–5 (2024). https://doi.org/10.1109/APSIPAASC58517.2024.10563841 [Google Scholar]
- B. Van Eden, N. Botha, B. Rosman, A comparison of visual place recognition methods using a mobile robot in an indoor environment. (Unpublished manuscript, 2023) [Google Scholar]
- B. Van Eden, N. Botha, Enhancing indoor place classification for mobile robots using RGB-D data and deep learning architectures. MATEC Web Conf. 406, 04002 (2024). https://doi.org/10.1051/matecconf/202440604002 [Google Scholar]
- J. Kapukotuwa, B. Lee, D. Devine, Y. Qiao, MultiROS: ROS-based robot simulation environment for concurrent deep reinforcement learning. In Proc. IEEE Int. Conf. Autom. Sci. Eng. (CASE), 1098–1103 (2022). https://doi.org/10.1109/CASE49997.2022.9926530 [Google Scholar]
- O. Hamed, M. Hamlich, Navigation method for autonomous mobile robots based on ROS and multi-robot improved Q-learning. Prog. Artif. Intell. 1–9 (2024). https://doi.org/10.1007/s13748-024-00377-2 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

