UAV-based Odor Source Localization in Multi-Building Environments Using Simulated Annealing Algorithm

Yang, Zi-Zhen; Jing, Tao; Meng, Qing-Hao

doi:10.23919/ccc50068.2020.9189425

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The VNAs communicated with a computer where MATLAB software reads the measurements as soon as they were available. Additional details of the system can be found in previous work [11], [28].…”

Section: Table I Qcm Sensors and Their Frequency Shift Due To Film Co...mentioning

confidence: 99%

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Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender

Aleixandre

Nakamoto

2022

IEEE Sensors J.

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and the Ph.D. degree in automatic and informatics industrial engineering from the Universidad Nacional de Educacion a Distancia, Madrid, in 2007. He worked with several departments at the Spanish Consejo Superior de Investigaciones Cientificas, Madrid, from 2000 to 2017, researching gas sensors, aerosol, and analysis of electromyography signals. He worked with Sgenia, Madrid, as a Researcher Engineer, developing electronic noses in the health field from 2014 to 2015. Since 2018, he has been working as a Researcher with

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Section: Table I Qcm Sensors and Their Frequency Shift Due To Film Co...mentioning

confidence: 99%

“…It can be done for several different topics, such as increasing accuracy or locating objects. Some of the parameters that could be modified are sensor temperature [10], location [11], or, in this work, an odorous gas mixture.…”

Section: Introductionmentioning

confidence: 99%

Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender

Aleixandre

Nakamoto

2022

IEEE Sensors J.

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“…Instead of using reactive methods, heuristic optimization may be a better alternative solution as historical samplings are used. [7,8], [9], [10], [8]. They used a distributed nano multi-quadcopter in indoor cluttered GPS-denied environ-ment.…”

Section: Introductionmentioning

confidence: 99%

“…Computational Fluid Dynamics (CFD) simulation and LSTM-RNN method are utilized by [11] to allow a robot predicts the source location in a chemical plant that has complex terrain. Simulated annealing method was used by [23] in a multi-building environment. However, there is no comprehensive evaluation as it only used one simple wind profile.…”

Section: Introductionmentioning

confidence: 99%

Integration of Bayesian Inference and Anemotaxis for Robotics Gas Source Localization in a Large Cluttered Outdoor Environment

et al. 2023

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Finding a gas source in a cluttered outdoor environment using autonomous robot is a complex challenge. The gas movement is difficult to predict as it is significantly affected by the wind and the shape of objects in the environment. In this paper, we propose a new probabilistic model and an integration of Bayesian inference and anemotaxis methods used for a robot to find a gas source in a large cluttered outdoor environment. An autonomous robot installed with a gas sensor is expected to find the location of the gas source after the gas leak occurs for a particular time. The advantage of the Bayesian inference technique has been presented previously so that a robot can find the gas source in an isolated indoor building without any significant wind flow. The large environment is divided into some particular regions. A set of probability density function was collected previously from a large amount of gas dispersion simulation to estimate the maximum likelihood of where the gas source is. The challenge gets more extensive if the Bayesian inference method is applied in an outdoor and cluttered environment. Instead of only measuring the gas concentration, the wind angle is also used as the wind profile significantly affects the gas dispersion. Therefore, the probability model is modified to allow the wind direction as a new variable. Moreover, an anemotaxis method is incorporated as the decision-making support as it may be more efficient to direct the robot explicitly to the upwind direction. Evaluations of the proposed method were carried out and its advantage was shown through simulation in a number of different scenarios.

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“…The algorithm encodes the position of the robot and uses operators such as crossover, mutation, and selection to calculate the target point of the robot to obtain the location of the odor source. Yang et al (2020) introduced a simulated annealing algorithm for problems such as large search space, turbulent air flow in the building environment, and local concentrated extreme values near the building walls. The algorithm adds a local extreme value judgment to the search process.…”

Section: Introductionmentioning

confidence: 99%

Rapid location technology of odor sources by multi‐UAV

Liu

Xiu-ling

et al. 2022

Journal of Field Robotics

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A multi-unmanned aerial vehicle (UAV) offers the advantages of flexibility, safety, and efficiency with respect to odor source localization. When the traditional particle swarm optimization (PSO) algorithm is used to locate the odor source, the search efficiency of the UAVs is reduced owing to unplanned search routes, and local extreme points and internal collisions may occur, making it impossible to quickly locate the odor source. In response to these problems, this paper proposes an improved particle swarm optimization (IPSO) algorithm, which combines the PSO with the Zigzag algorithm to preplan the search routes and improve the efficiency of the UAVs to search the plume. In addition, the distance between the UAVs is used as an adjustment factor to adaptively adjust the inertia weights of the particles to prevent the UAVs from falling into a local optimum during the plume-tracking stage and further improve the positioning efficiency. Finally, the PSO combined with the artificial potential field algorithm is used to avoid the internal collision of the UAVs when tracking the plume. The algorithm is verified through simulations and physical experiments. The results show that the IPSO significantly improves the efficiency and can achieve rapid and effective location of the odor source, which verifies the effectiveness and applicability of the algorithm.

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UAV-based Odor Source Localization in Multi-Building Environments Using Simulated Annealing Algorithm

Cited by 4 publications

References 20 publications

Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender

Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender

Integration of Bayesian Inference and Anemotaxis for Robotics Gas Source Localization in a Large Cluttered Outdoor Environment

Rapid location technology of odor sources by multi‐UAV

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