Affiliation:
1. Faculty of Mechanical Engineering, University of Prishtina , Department of Mechatronics , Str. Kodra e Diellit, n.n ., Prishtina , Kosovo
Abstract
Abstract
Several algorithms such as A*, Dijkstra, SLAM (Simultaneous Localisation and Mapping) and APF (Artificial Potential Field) were used in this study to create local maps, plan the shortest path, and localize mobile robots. In fact, when compared to the SLAM/APF method, these algorithms achieved a reduction in road length by 1.18 meters. Nevertheless, the SLAM/APF method outperformed the Dijkstra algorithm by reducing navigation time by 7.62 seconds and surpassed the A* method by reducing navigation time by 5.76 seconds.
Reference20 articles.
1. Kamil Shehata, F K., A, H. H., El-Batsh, H. M. “Mobile robot obstacle avoidance based on neural network with a standardization technique”, Journal of Robotics, pp. 1 – 14, 2021.
2. Gunaza, W., Zhongmin, W., Yu., Y. “Optimization of SLAM Gmapping based on simulation”, International Journal of Engineering Research & Technology 9, pp. 74 – 81, 2020.
3. Tim, B., Durrant-Whyte, H. “Simultaneous localization and mapping (SLAM): Part II”, IEEE robotics & automation magazine 13 (3), pp. 108 – 117, 2006.
4. Theodoros, T., Hu, H., McDonald-Maier, K., Gu. D. “Kinect enabled monte carlo localisation for a robotic wheelchair”, Frontiers of Intelligent Autonomous Systems, pp. 17 – 27, 2013.
5. Marcell, M., Bennewitz, M. “Predictive collision avoidance for the dynamic window approach.” International Conference on Robotics and Automation (ICRA), pp. 8620 – 8626, IEEE, 2019.