Improvement of the sensory and autonomous capability of robots through olfaction: the IRO Project

Olfaction is a valuable source of information about the environment that has not been sufficiently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efficiency, autonomy and usefulness. Copyright c © CEA.

[1]  Javier Gonzalez Monroy,et al.  GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments , 2017, Sensors.

[2]  R. Andrew Russell,et al.  Robot Odor Localization: A Taxonomy and Survey , 2008, Int. J. Robotics Res..

[3]  Xuan-Tung Truong,et al.  “To Approach Humans?”: A Unified Framework for Approaching Pose Prediction and Socially Aware Robot Navigation , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[4]  Silvia Coradeschi,et al.  Odour classification system for continuous monitoring applications , 2009 .

[5]  Cipriano Galindo,et al.  Multi-hierarchical semantic maps for mobile robotics , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Javier Gonzalez Monroy,et al.  An Electronic Architecture for Multipurpose Artificial Noses , 2018, J. Sensors.

[7]  A. Torralba,et al.  The role of context in object recognition , 2007, Trends in Cognitive Sciences.

[8]  Javier Gonzalez-Jimenez,et al.  Probabilistic gas quantification with MOX sensors in Open Sampling Systems—A Gaussian Process approach , 2013 .

[9]  José-Raúl Ruiz-Sarmiento,et al.  Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living , 2016, UCAmI.

[10]  G. Shepherd The Human Sense of Smell: Are We Better Than We Think? , 2004, PLoS biology.

[11]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[12]  Moshe Kam,et al.  Sensor Fusion for Mobile Robot Navigation , 1997, Proc. IEEE.

[13]  Alessandro Saffiotti,et al.  Inferring robot goals from violations of semantic knowledge , 2013, Robotics Auton. Syst..

[14]  N. Bârsan,et al.  Electronic nose: current status and future trends. , 2008, Chemical reviews.

[15]  Javier Gonzalez-Jimenez,et al.  Gas classification in motion: An experimental analysis , 2017 .

[16]  José-Raúl Ruiz-Sarmiento,et al.  A survey on learning approaches for Undirected Graphical Models. Application to scene object recognition , 2017, Int. J. Approx. Reason..

[17]  Lino Marques,et al.  Olfaction-based mobile robot navigation , 2002 .

[18]  Lothar Thiele,et al.  Deriving high-resolution urban air pollution maps using mobile sensor nodes , 2015 .

[19]  R. Nowak,et al.  Accuracy and usefulness of a breath alcohol analyzer. , 1984, Annals of emergency medicine.

[20]  Javier Gonzalez Monroy,et al.  The Multi-Chamber Electronic Nose—An Improved Olfaction Sensor for Mobile Robotics , 2011, Sensors.

[21]  Antonios Gasteratos,et al.  Semantic mapping for mobile robotics tasks: A survey , 2015, Robotics Auton. Syst..

[22]  Nir Friedman,et al.  Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning , 2009 .

[23]  M P Hlastala,et al.  The alcohol breath test--a review. , 1998, Journal of applied physiology.

[24]  Javier González,et al.  Olfactory telerobotics. A feasible solution for teleoperated localization of gas sources? , 2019, Robotics Auton. Syst..

[25]  David Zhang,et al.  A Novel Breath Analysis System Based on Electronic Olfaction , 2010, IEEE Transactions on Biomedical Engineering.

[26]  Kiran Chikkadi,et al.  E-Nose Sensing of Low-ppb Formaldehyde in Gas Mixtures at High Relative Humidity for Breath Screening of Lung Cancer? , 2016 .

[27]  Michael I. Jordan,et al.  Loopy Belief Propagation for Approximate Inference: An Empirical Study , 1999, UAI.

[28]  Marco Trincavelli,et al.  Gas Discrimination for Mobile Robots , 2011, KI - Künstliche Intelligenz.

[29]  José-Raúl Ruiz-Sarmiento,et al.  Robot@Home, a robotic dataset for semantic mapping of home environments , 2017, Int. J. Robotics Res..

[30]  Javier Gonzalez Monroy,et al.  Odor recognition in robotics applications by discriminative time-series modeling , 2015, Pattern Analysis and Applications.

[31]  Hiroshi Ishida,et al.  Mobile robot navigation using vision and olfaction to search for a gas/odor source , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[32]  Javier Gonzalez-Jimenez,et al.  Towards Odor-Sensitive Mobile Robots , 2018 .

[33]  José-Raúl Ruiz-Sarmiento,et al.  A Semantic-Based Gas Source Localization with a Mobile Robot Combining Vision and Chemical Sensing , 2018, Sensors.

[34]  Jun Wang,et al.  Qualitative and quantitative analysis on aroma characteristics of ginseng at different ages using E-nose and GC-MS combined with chemometrics. , 2015, Journal of pharmaceutical and biomedical analysis.

[35]  José-Raúl Ruiz-Sarmiento,et al.  Towards a Semantic Gas Source Localization Under Uncertainty , 2018, IPMU.

[36]  Javier Gonzalez-Jimenez,et al.  Continuous chemical classification in uncontrolled environments with sliding windows , 2016 .

[37]  Carlos Sanchez-Garrido,et al.  A configurable smart e-nose for spatio-temporal olfactory analysis , 2014, IEEE SENSORS 2014 Proceedings.

[38]  Javier Gonzalez-Jimenez,et al.  A robotic experiment toward understanding human gas-source localization strategies , 2017, 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN).

[39]  Javier Gonzalez Monroy,et al.  Overcoming the Slow Recovery of MOX Gas Sensors through a System Modeling Approach , 2012, Sensors.

[40]  Michael Uschold,et al.  Ontologies: principles, methods and applications , 1996, The Knowledge Engineering Review.

[41]  Carlos Sanchez-Garrido,et al.  Monitoring household garbage odors in urban areas through distribution maps , 2014, IEEE SENSORS 2014 Proceedings.

[42]  Nikolai F. Rulkov,et al.  On the performance of gas sensor arrays in open sampling systems using Inhibitory Support Vector Machines , 2013 .

[43]  Ana Paiva,et al.  Social Robots for Long-Term Interaction: A Survey , 2013, International Journal of Social Robotics.

[44]  Seref Naci Engin,et al.  Determination of the relationship between sewage odour and BOD by neural networks , 2005, Environ. Model. Softw..

[45]  Serge J. Belongie,et al.  Context based object categorization: A critical survey , 2010, Comput. Vis. Image Underst..

[46]  José-Raúl Ruiz-Sarmiento,et al.  Building Multiversal Semantic Maps for Mobile Robot Operation , 2017, Knowl. Based Syst..

[47]  Javier Gonzalez Monroy,et al.  Integrating olfaction in a robotic telepresence loop , 2017, 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[48]  Ricardo Gutierrez-Osuna,et al.  A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors , 1999, IEEE Trans. Syst. Man Cybern. Part B.