2026-08-29T14:55:26Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/176442024-09-21T19:40:01Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927

00925njm 22002777a 4500 dc Monroy, Javier author Ruiz-Sarmiento, Jose-Raul author Moreno, Francisco-Angel author Melendez-Fernandez, Francisco author Galindo, Cipriano author Gonzalez-Jimenez, Javier author 2018-11-28 This paper addresses the localization of a gas emission source within a real-world human environment with a mobile robot. Our approach is based on an efficient and coherent system that fuses different sensor modalities (i.e., vision and chemical sensing) to exploit, for the first time, the semantic relationships among the detected gases and the objects visually recognized in the environment. This novel approach allows the robot to focus the search on a finite set of potential gas source candidates (dynamically updated as the robot operates), while accounting for the non-negligible uncertainties in the object recognition and gas classification tasks involved in the process. This approach is particularly interesting for structured indoor environments containing multiple obstacles and objects, enabling the inference of the relations between objects and between objects and gases. A probabilistic Bayesian framework is proposed to handle all these uncertainties and semantic relations, providing an ordered list of candidates to be the source. This candidate list is updated dynamically upon new sensor measurements to account for objects not previously considered in the search process. The exploitation of such probabilities together with information such as the locations of the objects, or the time needed to validate whether a given candidate is truly releasing gases, is delegated to a path planning algorithm based on Markov decision processes to minimize the search time. The system was tested in an office-like scenario, both with simulated and real experiments, to enable the comparison of different path planning strategies and to validate its efficiency under real-world conditions. 10.3390/s18124174 1424-8220 Sensors (Basel, Switzerland) http://hdl.handle.net/10668/13255 30487414 http://hdl.handle.net/20.500.12105/17644 Artificial Intelligence Bayes MDP Machine Learning Markov decision process Mobile Robot Olfaction Chemical sensors e-nose Electronic nose Gas classification Gas sensor Gas source localization Mobile robot Object recognition Semantics Sensor fusion Uncertainty Uncertainty propagation A Semantic-Based Gas Source Localization with a Mobile Robot Combining Vision and Chemical Sensing.