APA 6th Edition Al khawaldah, M. i Nuechter, A. (2014). Multi-Robot Cooperation for Efficient Exploration. Automatika, 55 (3), 276-286. https://doi.org/10.7305/automatika.2014.12.648
MLA 8th Edition Al khawaldah, Mohammad i Andreas Nuechter. "Multi-Robot Cooperation for Efficient Exploration." Automatika, vol. 55, br. 3, 2014, str. 276-286. https://doi.org/10.7305/automatika.2014.12.648. Citirano 23.10.2021.
Chicago 17th Edition Al khawaldah, Mohammad i Andreas Nuechter. "Multi-Robot Cooperation for Efficient Exploration." Automatika 55, br. 3 (2014): 276-286. https://doi.org/10.7305/automatika.2014.12.648
Harvard Al khawaldah, M., i Nuechter, A. (2014). 'Multi-Robot Cooperation for Efficient Exploration', Automatika, 55(3), str. 276-286. https://doi.org/10.7305/automatika.2014.12.648
Vancouver Al khawaldah M, Nuechter A. Multi-Robot Cooperation for Efficient Exploration. Automatika [Internet]. 2014 [pristupljeno 23.10.2021.];55(3):276-286. https://doi.org/10.7305/automatika.2014.12.648
IEEE M. Al khawaldah i A. Nuechter, "Multi-Robot Cooperation for Efficient Exploration", Automatika, vol.55, br. 3, str. 276-286, 2014. [Online]. https://doi.org/10.7305/automatika.2014.12.648
Sažetak This paper addresses the problem of exploration of an unknown environment by developing effective exploration strategies for a team of mobile robots equipped with continuously rotating 3D scanners. The main aim of the new strategies is to reduce the exploration time of unknown environment. Unlike most of other published works, to save time, the laser scanners rotate and scan the environment while robots are in motion. Furthermore, the new strategies are able to explore large outdoor environments as a considerable reduction of the required computations, especially those required for path planning, have been achieved. Moreover, another new exploration strategy has been developed so that robots continuously replan the order to visit the remaining unexplored areas according to the new data (i.e. updated map) collected by the robot in question or by the other team members. This new extension led to further enhancements over the above mentioned ones, but with slightly higher computational costs. Finally, to assess our new exploration strategies with different levels of environment complexity, new set of experiments were conducted in environments where obstacles are distributed according to the Hilbert curve. The results of these experiments show the effectiveness of the proposed technique to effectively distribute the robots over the environment. More importantly, we show how the optimal number of robots is related to the environment complexity.