@article {RN93,
	title = {Autonomous Data Collection Using a Self-Organizing Map},
	volume = {29},
	number = {5},
	year = {2018},
	pages = {1703-1715},
	type = {Journal Article},
	abstract = {The self-organizing map (SOM) is an unsupervised learning technique providing a transformation of a high-dimensional input space into a lower dimensional output space. In this paper, we utilize the SOM for the traveling salesman problem (TSP) to develop a solution to autonomous data collection. Autonomous data collection requires gathering data from predeployed sensors by moving within a limited communication radius. We propose a new growing SOM that adapts the number of neurons during learning, which also allows our approach to apply in cases where some sensors can be ignored due to a lower priority. Based on a comparison with available combinatorial heuristic algorithms for relevant variants of the TSP, the proposed approach demonstrates improved results, while also being less computationally demanding. Moreover, the proposed learning procedure can be extended to cases where particular sensors have varying communication radii, and it can also be extended to multivehicle planning.},
	keywords = {Data collection, Neurons, Planning, Robot sensing systems, Urban areas},
	doi = {10.1109/tnnls.2017.2678482},
	url = {https://app.dimensions.ai/details/publication/pub.1084207070},
	author = {Faigl, Jan and Hollinger, Geoffrey A.}
}
