A Mobile Robots PSO-based for Odor Source Localization in Dynamic Advection-Diffusion Environment

2006 ◽  
Author(s):  
Wisnu Jatmiko ◽  
Kosuke Sekiyama ◽  
Toshio Fukuda
Keyword(s):  
Mobile Robots ◽  
Source Localization ◽  
Odor Source ◽  
Advection Diffusion

Odor Source Localization Research of Mobile Robots in Indoor Environments

2013 ◽  
Vol 441 ◽  
pp. 796-800
Author(s):  
Chun Shu Li ◽  
Zhi Hua Yang ◽  
Gen Qun Cui ◽  
Bo Jin
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Source Localization ◽  
Control Strategies ◽  
Grid Method ◽  
Indoor Environments ◽  
Odor Source ◽  
Localization Algorithm ◽  
Plume Tracing ◽  
Short Time

Aiming at the odor source localization in an obstacle-filled wind-varying indoor environment, a new method based odor source localization algorithm for a single mobile robot is proposed. With the information of the wind and the concentration gradient, Wasps can find odor source in a short time. However, it is very difficult for mobile robots to mimic the behaviors of wasps exactly. So, besides the bionics, BP neural network is adopted for the mobile robot to find the odor source. The control strategies for the plume-tracing mobile robot are proposed which include the intelligent plume-tracing algorithm and the collision avoidance algorithm based on improved potential grid method. The algorithms were integrated to control the robot trace plumes in obstructed indoor environments. Experimental results have demonstrated the capability of this kind of plume-tracing mobile robot.

Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization

2018 ◽  
pp. 276-302
Author(s):  
Upma Jain ◽  
Ritu Tiwari ◽  
W. Wilfred Godfrey
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robots ◽  
Source Localization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Grey Wolf Optimizer ◽  
Odor Source ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Gravitational Search

This chapter concerns the problem of odor source localization by a team of mobile robots. A brief overview of odor source localization is given which is followed by related work. Three methods are proposed for odor source localization. These methods are largely inspired by gravitational search algorithm, grey wolf optimizer, and particle swarm optimization. Objective of the proposed approaches is to reduce the time required to localize the odor source by a team of mobile robots. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. Robots start search from the corner of the workspace. As robots enter in the vicinity of plume area, they form groups using K-nearest neighbor algorithm. To avoid stagnation of the robots at local optima, search counter concept is used. Proposed approaches are tested and validated through simulation.

Odor Source Localization Using Multiple Robots in Complicated City-Like Environments

2011 ◽  
Vol 291-294 ◽  
pp. 3337-3344 ◽  
Author(s):  
Zhen Zhang Liu ◽  
Yi Jun Wang ◽  
Tien Fu Lu
Keyword(s):  
Mobile Robots ◽  
Source Localization ◽  
Human Life ◽  
Real Life ◽  
Source Location ◽  
Emission Source ◽  
Odor Source ◽  
Multiple Robots ◽  
Plume Tracing

The detection of a dangerous emission source location has the potential to be enhanced by using plume-tracing mobile robots, without endangering human life during the detection and source localization process. So far, many researchers focus on odor source localization in simple & laboratory based environments. The present study focuses on more real life odor source localization scenarios. In this study, multiple robots were used and coordinated by a supervisory program to locate an odor source in complicated city-like environments. A series of simulations has been conducted and the results demonstrated the potential of the supervisory program to effectively control a number of robots to locate a dangerous odor source in real life scenarios.

Application of a Nature-Inspired Algorithm in Odor Source Localization

2020 ◽  
pp. 51-66
Author(s):  
Kumar Gaurav ◽  
Ramanpreet Singh ◽  
Ajay Kumar ◽  
Ram Dayal
Keyword(s):  
Source Localization ◽  
Odor Source ◽  
Nature Inspired Algorithm
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