Article
Multidisciplinary Sciences
Aurore Loisy, Christophe Eloy
Summary: Infotaxis is a search algorithm designed to track the source of odour in a turbulent environment using odour detections. This study provides an extensive review of infotaxis and presents a toolkit for devising better strategies. The results show that infotaxis is reliable, efficient, and safe, and the study suggests three possible ways to improve infotaxis using methods from artificial intelligence. Overall, the margin of improvement for infotaxis decreases as the dimensionality increases.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2022)
Article
Construction & Building Technology
Yatai Ji, Yong Zhao, Bin Chen, Zhengqiu Zhu, Yu Liu, Hai Zhu, Sihang Qiu
Summary: This paper proposes an active source searching framework for autonomous mobile robots to effectively search for targets in unknown obstructed environments. The framework includes source estimation, target determination, and path planning steps to achieve a balance between exploration and exploitation.
BUILDING AND ENVIRONMENT
(2022)
Article
Chemistry, Analytical
Yanru Zhao, Dongsheng Wang, Xiaojie Huang
Summary: Research on improving the precision of gas detection and developing effective search strategies was conducted based on a gas sensor array. The array was designed to mimic the artificial olfactory system and establish a one-to-one response mode to measured gases with inherent cross-sensitive properties. Quantitative identification algorithms were studied, and an improved Back Propagation algorithm combining cuckoo algorithm and simulated annealing algorithm was proposed. Test results demonstrated that the improved algorithm achieved the optimal solution with 0% error at the 424th iteration of the Schaffer function. The gas detection system designed using MATLAB effectively detected alcohol and methane concentrations within the detection range, exhibiting good performance.
Article
Computer Science, Information Systems
Feng Gao, Yeyun Cai, Fang Deng, Chengpu Yu, Jie Chen
Summary: This paper proposes an adaptive initialization method for sound source localization to reduce the potential existence range of a target and improve positioning accuracy. Experimental results demonstrate that the method can effectively reduce calculation time and enhance localization accuracy.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Review
Engineering, Electrical & Electronic
Tao Jing, Qing-Hao Meng, Hiroshi Ishida
Summary: This paper reviews the recent progress and development trends in the field of robot odor source localization, discussing research works grouped into four categories based on method principles. The paper also examines predictions of odor source direction and distance through in situ sensing and development of simulators, highlighting future outlooks for the field's flourishing.
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
(2021)
Article
Chemistry, Analytical
Daigo Terutsuki, Tomoya Uchida, Chihiro Fukui, Yuji Sukekawa, Yuki Okamoto, Ryohei Kanzaki
Summary: In this study, a fully autonomous small drone with a portable electroantennogram (EAG) based on silkmoth antennae was developed for real-time odorant concentration detection. Enhanced sensor directivity enabled the drone to recognize odor source locations without a wind direction sensor. This study proposes an efficient flight platform for detecting odorant molecules and localizing their sources.
SENSORS AND ACTUATORS B-CHEMICAL
(2021)
Article
Chemistry, Analytical
Duc-Nhat Luong, Daisuke Kurabayashi
Summary: Odor source localization (OSL) robots are essential in ensuring the safety of rescue teams exposed to hazardous chemical plumes. This study addresses the challenges of constructing odor dispersion models and dynamically modifying robot behavior for efficient source detection. By simplifying the environment and utilizing different algorithms, the proposed framework improves success rates and reduces search time. Experiments with an autonomous mobile robot demonstrate its effectiveness, achieving a 3.5 times increase in success rate and a 35% reduction in average moving steps.
Article
Engineering, Multidisciplinary
Xinxing Chen, Chenglong Fu, Jian Huang
Summary: This paper introduces the Deep Q-Network algorithm for odor source localization in indoor environments, demonstrating its advantages through 35,000 repeated training episodes and evaluations under different settings.
Article
Automation & Control Systems
Lingxiao Wang, Shuo Pang
Summary: This article introduces a behavior-based navigation algorithm for robotic odor source localization, which utilizes a fuzzy controller to adjust trajectory parameters in real-time based on the current search situation. The proposed algorithm outperforms classical olfactory-based navigation algorithms in terms of efficiency and success rate.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2022)
Article
Chemistry, Analytical
Shunsuke Shigaki, Mayu Yamada, Daisuke Kurabayashi, Koh Hosoda
Summary: We developed a novel algorithm inspired by the male silk moth, which enables a robot to locate odor sources indoors and outdoors. By measuring the female localization behavior of the silk moth using a virtual reality system, we identified two types of search behavior based on the direction of odor and wind detection. The robust moth-inspired algorithm was implemented on a ground-running robot and showed better localization performance compared to conventional moth-inspired algorithms in varying environmental complexities.
Article
Construction & Building Technology
Meh Jabeen, Qing-Hao Meng, Tao Jing, Hui-Rang Hou
Summary: This study proposes a robot-based odor source localization (OSL) algorithm that plans the localization through a controller, estimates the gradient of the odor plume concentration, and guides the robot to approach the odor source. The algorithm improves the searching efficiency and success rate of OSL, reducing casualties and property losses caused by leakage.
BUILDING AND ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Xinxing Chen, Yuquan Leng, Chenglong Fu
Summary: This paper proposes a training method for fuzzy inference systems in robotic control, using a supervised-reinforced successive training framework. The parameters are tuned and the system is initialized using a limited amount of input-output data from an existing controller. The evaluation results show that the performance of the system trained by this framework is superior to those trained by only supervised learning or reinforcement learning.
FRONTIERS IN NEUROROBOTICS
(2022)
Article
Environmental Sciences
Qingyuan Guo, Zhaoxia Li, Tianming Chen, Bairen Yang, Cheng Ding
Summary: Identifying odorants in the source water of QT River and exploring emergency disposal mechanisms, this study successfully identified 17 odorants present in both the source water and effluent of the drinking water treatment plant. Using powdered activated carbon (PAC) at 15 mg/L effectively removed medicinal, chemical, septic, and musty odors, offering a scientific basis for emergency odor management in similar situations.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Review
Computer Science, Artificial Intelligence
Kumar Gaurav, Prabhat Ranjan
Summary: This article discusses two approaches to studying source localization of odor cues, one is to use moth-inspired algorithms in robotic platforms, and the other is to use biosensors and insect-machine hybrid systems.
Article
Biology
Kalyanasundaram Parthasarathy, M. A. Willis
Summary: Insects are able to derive spatial information about odor distribution from bilateral comparisons between their antennae in flight, in addition to using timing cues. Experimental results show that hawkmoths can discriminate between odor stimuli arriving on either antenna with an accuracy of over 70%, indicating their capability to use spatial information in addition to temporal cues.
Article
Biochemical Research Methods
Vincent Jacob, Christelle Monsempes, Jean-Pierre Rospars, Jean-Baptiste Masson, Philippe Lucas
PLOS COMPUTATIONAL BIOLOGY
(2017)
Article
Behavioral Sciences
Julia Machon, Philippe Lucas, Juliette Ravaux, Magali Zbinden
Article
Cell & Tissue Engineering
Giuliana Gagliardi, Karim Ben M'Barek, Antoine Chaffiol, Amelie Slembrouck-Brec, Jean-Baptiste Conart, Celine Nanteau, Oriane Rabesandratana, Jose-Alain Sahel, Jens Duebel, Gael Orieux, Sacha Reichman, Olivier Goureau
Article
Biochemical Research Methods
Marie Levakova, Lubomir Kostal, Christelle Monsempes, Vincent Jacob, Philippe Lucas
PLOS COMPUTATIONAL BIOLOGY
(2018)
Article
Neurosciences
Marcela Garita-Hernandez, Laure Guibbal, Lyes Toualbi, Fiona Routet, Antoine Chaffiol, Celine Winckler, Marylin Harinquet, Camille Robert, Stephane Fouquet, Sebastien Bellow, Jose-Alain Sahel, Olivier Goureau, Jens Duebel, Deniz Dalkara
FRONTIERS IN NEUROSCIENCE
(2018)
Article
Neurosciences
Jeyathevy Sukiban, Nicole Voges, Till A. Dembek, Robin Pauli, Veerle Visser-Vandewalle, Michael Denker, Immo Weber, Lars Timmermann, Sonja Gruen
Review
Physiology
Lucie Conchou, Philippe Lucas, Camille Meslin, Magali Proffit, Michael Staudt, Michel Ronou
FRONTIERS IN PHYSIOLOGY
(2019)
Article
Multidisciplinary Sciences
Marcela Garita-Hernandez, Marusa Lampic, Antoine Chaffiol, Laure Guibbal, Fiona Routet, Tiago Santos-Ferreira, Sylvia Gasparini, Oliver Borsch, Giuliana Gagliardi, Sacha Reichman, Serge Picaud, Jose-Alain Sahel, Olivier Goureau, Marius Ader, Deniz Dalkara, Jens Duebel
NATURE COMMUNICATIONS
(2019)
Article
Cell Biology
Alice Louail, Martijn C. Sierksma, Antoine Chaffiol, Sarah Baudet, Ahlem Assali, Sandrine Couvet, Melissa Nedjam, Fiona Roche, Yvrick Zagar, Jens Duebel, Xavier Nicol
Article
Cell Biology
Oriane Rabesandratana, Antoine Chaffiol, Antoine Mialot, Amelie Slembrouck-Brec, Corentin Joffrois, Celine Nanteau, Amelie Rodrigues, Giuliana Gagliardi, Sacha Reichman, Jose-Alain Sahel, Alain Chedotal, Jens Duebel, Olivier Goureau, Gael Orieux
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2020)
Article
Multidisciplinary Sciences
M. Provansal, G. Labernede, C. Joffrois, A. Rizkallah, R. Goulet, M. Valet, W. Deschamps, U. Ferrari, A. Chaffiol, D. Dalkara, J. A. Sahel, M. Tanter, S. Picaud, G. Gauvain, F. Arcizet
Summary: This study demonstrates the detection of deep optogenetic activations in anesthetized rats' V1 using fUS imaging. The optogenetic specificity of these activations and their neuronal origin were confirmed through electrophysiological recordings. Furthermore, it was shown that the optogenetic response initiated in V1 spreads to downstream (LGN) and upstream (V2) visual areas.
SCIENTIFIC REPORTS
(2021)
Article
Medicine, Research & Experimental
Antoine Chaffiol, Matthieu Provansal, Corentin Joffrois, Kevin Blaize, Guillaume Labernede, Ruben Goulet, Emma Burban, Elena Brazhnikova, Jens Duebel, Pierre Pouget, Jose Alain Sahel, Serge Picaud, Fabrice Arcizet, Gregory Gauvain
Summary: Optogenetics has revolutionized neuroscience research and has great potential for therapeutic applications in vision restoration. In our study, we demonstrated long-term expression and functionality of optogenes in the retina of non-human primates, as well as successful signal transmission to the visual cortex. These findings are crucial for future clinical applications.
MOLECULAR THERAPY-METHODS & CLINICAL DEVELOPMENT
(2022)
Article
Biochemical Research Methods
Johanna Senk, Birgit Kriener, Mikael Djurfeldt, Nicole Voges, Han-Jia Jiang, Lisa Schuettler, Gabriele Gramelsberger, Markus Diesmann, Hans E. Plesser, Sacha J. van Albada
Summary: Sustainable research on computational models of neuronal networks requires understandable, reproducible, and extendable published models. However, missing details or ambiguities in mathematical concepts, algorithmic implementations, or parameterizations hinder progress. This work aims to provide complete and concise descriptions of network connectivity and guide the implementation of connection routines in simulation software and neuromorphic hardware systems. A review of existing models reveals a substantial proportion of ambiguous descriptions. Based on this review, a set of connectivity concepts is derived and a unified graphical notation is proposed to facilitate intuitive understanding of network properties. The proposed standardizations are expected to contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Antoine Chaffiol, Masaaki Ishii, Yu Cao, Stuart C. Mangel
Article
Medicine, Research & Experimental
Hanen Khabou, Marcela Garita-Hernandez, Antoine Chaffiol, Sacha Reichman, Celine Jaillard, Elena Brazhnikova, Stephane Bertin, Valerie Forster, Melissa Desrosiers, Celine Winckler, Olivier Goureau, Serge Picaud, Jens Duebel, Jose-Alain Sahel, Deniz Dalkara